{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "def load_housing_data(housing_path = './'):\n",
    "    csv_path = os.path.join(housing_path, 'housing.csv')\n",
    "    return pd.read_csv(csv_path)\n",
    "housing = load_housing_data()\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20640, 10)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.shape   # 数据数量和数据特征种数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   longitude           20640 non-null  float64\n",
      " 1   latitude            20640 non-null  float64\n",
      " 2   housing_median_age  20640 non-null  float64\n",
      " 3   total_rooms         20640 non-null  float64\n",
      " 4   total_bedrooms      20433 non-null  float64\n",
      " 5   population          20640 non-null  float64\n",
      " 6   households          20640 non-null  float64\n",
      " 7   median_income       20640 non-null  float64\n",
      " 8   median_house_value  20640 non-null  float64\n",
      " 9   ocean_proximity     20640 non-null  object \n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     9136\n",
       "INLAND        6551\n",
       "NEAR OCEAN    2658\n",
       "NEAR BAY      2290\n",
       "ISLAND           5\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total_bedrooms 20433 有空值\n",
    "housing['ocean_proximity'].value_counts()  # 查看多少种分类"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5种分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
       "std        2.003532      2.135952           12.585558   2181.615252   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
       "std        421.385070   1132.462122    382.329753       1.899822   \n",
       "min          1.000000      3.000000      1.000000       0.499900   \n",
       "25%        296.000000    787.000000    280.000000       2.563400   \n",
       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
       "\n",
       "       median_house_value  \n",
       "count        20640.000000  \n",
       "mean        206855.816909  \n",
       "std         115395.615874  \n",
       "min          14999.000000  \n",
       "25%         119600.000000  \n",
       "50%         179700.000000  \n",
       "75%         264725.000000  \n",
       "max         500001.000000  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\jiang\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\tools.py:298: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "c:\\users\\jiang\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\tools.py:298: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "c:\\users\\jiang\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\tools.py:304: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n",
      "c:\\users\\jiang\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\tools.py:304: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "housing.hist(bins = 50, figsize = (20,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 房龄和房价设定了上限\n",
    "## 重尾\n",
    "## 收入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 创建训练集和测试集\n",
    "# 训练集用于模型训练，占数据集的80%，测试集占20%\n",
    "import numpy as np\n",
    "import random\n",
    "def split_train_test(data, test_radio):\n",
    "    np.random.seed(42)    # 设置随机种子，确保每次得到的随机数确定\n",
    "    indices = np.random.permutation(len(data))   # 对原来的数组进行重新洗牌，随机打乱原来的元素顺序\n",
    "    test_set_size = int(len(data) * test_radio)\n",
    "    test_indices = indices[:test_set_size]\n",
    "    train_indices = indices[test_set_size:]\n",
    "    return data.iloc[train_indices], data.iloc[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set, test_set = split_train_test(housing, 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16512"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4128"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 对样本设置唯一的标识符，对标识符取哈希值，取哈希值的最后一个字节，值小于等于51,256*20%，放入测试集\n",
    "# 哈希值相等的两个对象，不一定是同一个对象，哈希值不等的两个对象，一定不是同一个对象\n",
    "import hashlib\n",
    "def test_set_check(identifier, test_radio, hash = hashlib.md5):\n",
    "    return hash(np.int64(identifier)).digest()[-1] < 256 * test_radio    # 返回摘要，作为二进制数据字符串"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_train_test_by_id(data, test_ratio, id_column):\n",
    "    ids = data[id_column]\n",
    "    in_test_set = ids.apply(lambda id_:test_set_check(id_, test_ratio))\n",
    "    return data.loc[~in_test_set], data.loc[in_test_set]     # ~in_test_set为取反操作，即除去[in_test_set]的部分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_with_id = housing.reset_index()    # 使用行索引作为标识符 ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "4      4    -122.25     37.85                52.0       1627.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "4           280.0       565.0       259.0         3.8462            342200.0   \n",
       "\n",
       "  ocean_proximity  \n",
       "0        NEAR BAY  \n",
       "1        NEAR BAY  \n",
       "2        NEAR BAY  \n",
       "3        NEAR BAY  \n",
       "4        NEAR BAY  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_with_id.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set, test_set = split_train_test_by_id(housing_with_id, 0.2, 'index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2535.0</td>\n",
       "      <td>489.0</td>\n",
       "      <td>1094.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>3.6591</td>\n",
       "      <td>299200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "6      6    -122.25     37.84                52.0       2535.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "6           489.0      1094.0       514.0         3.6591            299200.0   \n",
       "\n",
       "  ocean_proximity  \n",
       "0        NEAR BAY  \n",
       "1        NEAR BAY  \n",
       "2        NEAR BAY  \n",
       "3        NEAR BAY  \n",
       "6        NEAR BAY  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.head()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# 基于行索引，不能删除和中间插入数据，只能末尾插入，否则行索引会变\n",
    "# 寻找文档特征来创建唯一标识符，如经纬度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>index</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
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       "      <th>id</th>\n",
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       "      <th>0</th>\n",
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       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122192.12</td>\n",
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       "      <th>1</th>\n",
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       "      <td>-122.22</td>\n",
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       "      <td>NEAR BAY</td>\n",
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       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122202.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "4      4    -122.25     37.85                52.0       1627.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "4           280.0       565.0       259.0         3.8462            342200.0   \n",
       "\n",
       "  ocean_proximity         id  \n",
       "0        NEAR BAY -122192.12  \n",
       "1        NEAR BAY -122182.14  \n",
       "2        NEAR BAY -122202.15  \n",
       "3        NEAR BAY -122212.15  \n",
       "4        NEAR BAY -122212.15  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_with_id['id'] = housing['longitude'] * 1000 + housing['latitude']\n",
    "housing_with_id.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
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       "      <th>0</th>\n",
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       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
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       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2535.0</td>\n",
       "      <td>489.0</td>\n",
       "      <td>1094.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>3.6591</td>\n",
       "      <td>299200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "6      6    -122.25     37.84                52.0       2535.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "6           489.0      1094.0       514.0         3.6591            299200.0   \n",
       "\n",
       "  ocean_proximity  \n",
       "0        NEAR BAY  \n",
       "1        NEAR BAY  \n",
       "2        NEAR BAY  \n",
       "3        NEAR BAY  \n",
       "6        NEAR BAY  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22bdcaacc10>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分层采样\n",
    "# 即按照比例采样\n",
    "housing['median_income'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 期望使用测试集代表整个数据集各种不同类型的收入"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 创建一个收入类别属性\n",
    "2. 数据分层不应太多，且确保每层有足够数据量\n",
    "3. 将中位收入除以1.5，限制收入类别数量，使用ceil取整，得到离散类别，将大于5的列别合并为类别5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing['income_cat'] = np.ceil(housing['median_income']/1.5)\n",
    "housing['income_cat'].head(20)\n",
    "housing['income_cat'].where(housing['income_cat'] < 5, 5.0,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5.0\n",
       "1     5.0\n",
       "2     5.0\n",
       "3     4.0\n",
       "4     3.0\n",
       "5     3.0\n",
       "6     3.0\n",
       "7     3.0\n",
       "8     2.0\n",
       "9     3.0\n",
       "10    3.0\n",
       "11    3.0\n",
       "12    3.0\n",
       "13    2.0\n",
       "14    2.0\n",
       "15    2.0\n",
       "16    2.0\n",
       "17    2.0\n",
       "18    2.0\n",
       "19    2.0\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['income_cat'].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "housing['income_cat'] = pd.cut(housing['median_income'], bins=[0.,1.5,3.0,4.5,6.,np.inf], labels = [1,2,3,4,5])    # 吧连续值转换成类别标签（np.inf为无穷）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    7236\n",
       "2    6581\n",
       "4    3639\n",
       "5    2362\n",
       "1     822\n",
       "Name: income_cat, dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['income_cat'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22bddad4d60>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing['income_cat'].hist()   # 收入类别用直方图展示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit,train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "split = StratifiedShuffleSplit(n_splits=1,test_size = 0.2,random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "for train_index, test_index in split.split(housing, housing['income_cat']):\n",
    "    strat_train_set = housing.loc[train_index]\n",
    "    strat_test_set = housing.loc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16512, 11)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_train_set.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>286600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>340600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>196900.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>46300.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>254500.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19480</th>\n",
       "      <td>-120.97</td>\n",
       "      <td>37.66</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2930.0</td>\n",
       "      <td>588.0</td>\n",
       "      <td>1448.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>3.5395</td>\n",
       "      <td>127900.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8879</th>\n",
       "      <td>-118.50</td>\n",
       "      <td>34.04</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2233.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>769.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>8.3839</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13685</th>\n",
       "      <td>-117.24</td>\n",
       "      <td>34.15</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2041.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>140200.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4937</th>\n",
       "      <td>-118.26</td>\n",
       "      <td>33.99</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1865.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>1916.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>1.8242</td>\n",
       "      <td>95000.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4861</th>\n",
       "      <td>-118.28</td>\n",
       "      <td>34.02</td>\n",
       "      <td>29.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>2690.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>0.4999</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16365</th>\n",
       "      <td>-121.31</td>\n",
       "      <td>38.02</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4157.0</td>\n",
       "      <td>951.0</td>\n",
       "      <td>2734.0</td>\n",
       "      <td>879.0</td>\n",
       "      <td>2.7981</td>\n",
       "      <td>92100.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19684</th>\n",
       "      <td>-121.62</td>\n",
       "      <td>39.14</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2183.0</td>\n",
       "      <td>559.0</td>\n",
       "      <td>1202.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>1.6902</td>\n",
       "      <td>61500.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19234</th>\n",
       "      <td>-122.69</td>\n",
       "      <td>38.51</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3364.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>1442.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>6.6854</td>\n",
       "      <td>313000.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13956</th>\n",
       "      <td>-117.06</td>\n",
       "      <td>34.17</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2520.0</td>\n",
       "      <td>582.0</td>\n",
       "      <td>416.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>2.7120</td>\n",
       "      <td>89000.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2390</th>\n",
       "      <td>-119.46</td>\n",
       "      <td>36.91</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>1184.0</td>\n",
       "      <td>429.0</td>\n",
       "      <td>3.9141</td>\n",
       "      <td>123900.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11176</th>\n",
       "      <td>-117.96</td>\n",
       "      <td>33.83</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2838.0</td>\n",
       "      <td>649.0</td>\n",
       "      <td>1758.0</td>\n",
       "      <td>593.0</td>\n",
       "      <td>3.3831</td>\n",
       "      <td>197400.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15614</th>\n",
       "      <td>-122.41</td>\n",
       "      <td>37.81</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1178.0</td>\n",
       "      <td>545.0</td>\n",
       "      <td>592.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>3.6728</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2953</th>\n",
       "      <td>-119.02</td>\n",
       "      <td>35.35</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1239.0</td>\n",
       "      <td>251.0</td>\n",
       "      <td>776.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>1.9830</td>\n",
       "      <td>63300.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13209</th>\n",
       "      <td>-117.72</td>\n",
       "      <td>34.05</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1841.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>1243.0</td>\n",
       "      <td>394.0</td>\n",
       "      <td>4.0614</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6569</th>\n",
       "      <td>-118.15</td>\n",
       "      <td>34.20</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1505.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>857.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>4.5000</td>\n",
       "      <td>184200.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "19480    -120.97     37.66                24.0       2930.0           588.0   \n",
       "8879     -118.50     34.04                52.0       2233.0           317.0   \n",
       "13685    -117.24     34.15                26.0       2041.0           293.0   \n",
       "4937     -118.26     33.99                47.0       1865.0           465.0   \n",
       "4861     -118.28     34.02                29.0        515.0           229.0   \n",
       "16365    -121.31     38.02                24.0       4157.0           951.0   \n",
       "19684    -121.62     39.14                41.0       2183.0           559.0   \n",
       "19234    -122.69     38.51                18.0       3364.0           501.0   \n",
       "13956    -117.06     34.17                21.0       2520.0           582.0   \n",
       "2390     -119.46     36.91                12.0       2980.0           495.0   \n",
       "11176    -117.96     33.83                30.0       2838.0           649.0   \n",
       "15614    -122.41     37.81                25.0       1178.0           545.0   \n",
       "2953     -119.02     35.35                42.0       1239.0           251.0   \n",
       "13209    -117.72     34.05                 8.0       1841.0           409.0   \n",
       "6569     -118.15     34.20                46.0       1505.0           261.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "17606       710.0       339.0         2.7042            286600.0   \n",
       "18632       306.0       113.0         6.4214            340600.0   \n",
       "14650       936.0       462.0         2.8621            196900.0   \n",
       "3230       1460.0       353.0         1.8839             46300.0   \n",
       "3555       4459.0      1463.0         3.0347            254500.0   \n",
       "19480      1448.0       570.0         3.5395            127900.0   \n",
       "8879        769.0       277.0         8.3839            500001.0   \n",
       "13685       936.0       375.0         6.0000            140200.0   \n",
       "4937       1916.0       438.0         1.8242             95000.0   \n",
       "4861       2690.0       217.0         0.4999            500001.0   \n",
       "16365      2734.0       879.0         2.7981             92100.0   \n",
       "19684      1202.0       506.0         1.6902             61500.0   \n",
       "19234      1442.0       506.0         6.6854            313000.0   \n",
       "13956       416.0       151.0         2.7120             89000.0   \n",
       "2390       1184.0       429.0         3.9141            123900.0   \n",
       "11176      1758.0       593.0         3.3831            197400.0   \n",
       "15614       592.0       441.0         3.6728            500001.0   \n",
       "2953        776.0       272.0         1.9830             63300.0   \n",
       "13209      1243.0       394.0         4.0614            107000.0   \n",
       "6569        857.0       269.0         4.5000            184200.0   \n",
       "\n",
       "      ocean_proximity income_cat  \n",
       "17606       <1H OCEAN          2  \n",
       "18632       <1H OCEAN          5  \n",
       "14650      NEAR OCEAN          2  \n",
       "3230           INLAND          2  \n",
       "3555        <1H OCEAN          3  \n",
       "19480          INLAND          3  \n",
       "8879        <1H OCEAN          5  \n",
       "13685          INLAND          4  \n",
       "4937        <1H OCEAN          2  \n",
       "4861        <1H OCEAN          1  \n",
       "16365          INLAND          2  \n",
       "19684          INLAND          2  \n",
       "19234       <1H OCEAN          5  \n",
       "13956          INLAND          2  \n",
       "2390           INLAND          3  \n",
       "11176       <1H OCEAN          3  \n",
       "15614        NEAR BAY          3  \n",
       "2953           INLAND          2  \n",
       "13209          INLAND          3  \n",
       "6569           INLAND          3  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_train_set.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    0.350533\n",
       "2    0.318798\n",
       "4    0.176357\n",
       "5    0.114583\n",
       "1    0.039729\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看所有住房数据根据收入类别比例分布，收入占类别百分比\n",
    "strat_test_set['income_cat'].value_counts()/len(strat_test_set)  # 分层抽样测试集合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    0.350581\n",
       "2    0.318847\n",
       "4    0.176308\n",
       "5    0.114438\n",
       "1    0.039826\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['income_cat'].value_counts()/len(housing)  # 完整数据集合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "def income_cat_proprtions(data):\n",
    "    return data['income_cat'].value_counts() / len(data)\n",
    "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)\n",
    "compare_props = pd.DataFrame({\n",
    "    \"全部数据\": income_cat_proprtions(housing),\n",
    "    \"分层抽样\": income_cat_proprtions(strat_test_set),\n",
    "    \"随机抽样\": income_cat_proprtions(test_set),\n",
    "}).sort_index()\n",
    "compare_props[\"随机. %error\"] = 100 * compare_props[\"随机抽样\"] / compare_props[\"全部数据\"] - 100\n",
    "compare_props[\"分层. %error\"] = 100 * compare_props[\"分层抽样\"] / compare_props[\"全部数据\"] - 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>全部数据</th>\n",
       "      <th>分层抽样</th>\n",
       "      <th>随机抽样</th>\n",
       "      <th>随机. %error</th>\n",
       "      <th>分层. %error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.039826</td>\n",
       "      <td>0.039729</td>\n",
       "      <td>0.040213</td>\n",
       "      <td>0.973236</td>\n",
       "      <td>-0.243309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.318847</td>\n",
       "      <td>0.318798</td>\n",
       "      <td>0.324370</td>\n",
       "      <td>1.732260</td>\n",
       "      <td>-0.015195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.350581</td>\n",
       "      <td>0.350533</td>\n",
       "      <td>0.358527</td>\n",
       "      <td>2.266446</td>\n",
       "      <td>-0.013820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.176308</td>\n",
       "      <td>0.176357</td>\n",
       "      <td>0.167393</td>\n",
       "      <td>-5.056334</td>\n",
       "      <td>0.027480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.114438</td>\n",
       "      <td>0.114583</td>\n",
       "      <td>0.109496</td>\n",
       "      <td>-4.318374</td>\n",
       "      <td>0.127011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       全部数据      分层抽样      随机抽样  随机. %error  分层. %error\n",
       "1  0.039826  0.039729  0.040213    0.973236   -0.243309\n",
       "2  0.318847  0.318798  0.324370    1.732260   -0.015195\n",
       "3  0.350581  0.350533  0.358527    2.266446   -0.013820\n",
       "4  0.176308  0.176357  0.167393   -5.056334    0.027480\n",
       "5  0.114438  0.114583  0.109496   -4.318374    0.127011"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_props"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22bdcf06e80>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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otI5BrefjjTC/4PFLQSNjJ2RSXJCBXt0duGfFJ8FmN0AOAd0xKgvLth+WTRcFlI25VwIgSdhR3QAOgNkSKSHYMHsMKr4+g9HPbAVHSNQJdEDJVaidF8X8d8qx9bOTEGwksRX58dQkR9ARRKJHFGqHb+aAeUKw7dBJXDO8r2lDHytXZTDiQ6fMMej1fKJBbWSmjczUPDetMNNgfHLTUyHpJCnO+/14bcfhkOEegSN44LphpjpHLp5DXWNL8LOci/KzKJh1LG8sP47zPkkj3Z3s5OESiGFNLT4/Zi4vMai82kH9b2KmkWTmgJu9Ih5/+7+44ndbsHLPEU3ugZWrMhjxo1M6BrMkbaQoRqahyYNV+7X1/KtKagzJUXWC1RUwqAQcfGF2+F6/iNuLsvDe/WMNiWyvKOHwN82m2kQuIfLPN2F437CvSXbxWFici48emYDnb80PJoxdAgEhstxHNOJ34cQA1d+fes62R6TwinJH+u2v7saVv/8AL3/wOU41ezH+Eu3nMXPYDAYjcjqlY7AqJbWLSyDBRGo4g6bQ0OTBQHcyVtz1/WAzl9dkh26AEOyqrpdzFSpDLE9yk/Dcps8Mpx6XwOH5W0dAsFGRpOZHlw8I61D8ooS+PS/kEJSZBq/OKEQ3gde81ux7sMJuSG7XvPH4n6uHmt6j2SvB46d4fnMVrn1hOzaWH9c8b+awGQxG5HRKx2BWHlmcfxEEnqC7k0OocHp3J49XZxSiuCDDUoNIb9DU5ay3/+Vjw9yGUKi7kxXD+Mr074EjcjWRPsQjd1+PwJT8jICktX2OnWrB4qna72XGFVnBnwVObm67d+WnmrLc/AG9kJueajt0o3Ryb686ieoTZ1F27HRgh99H87oJlxhPMO4UFyq/ji7JHomjYjAY1nTa5LO6YkbpQO4myMqej0wcrhGGUyNRitz0VIMGkcABLoGHT5Qwf/IFXSDTpjMdLoHDjy8fgL9/fBQcRzQD7QFtPsOd4kJqkhNOng9KVQOBEM9NuZpE7N1jh2DZ9tA5DP06zCqJfjlhGCrqzmDm8hJ4/FKwm1td5WO3PHVtaS0e/GepZk08MS+r3VB+HFsOncDiqfnBJHRDkwebKk/a+0A61OE/1vTGYERPp3UMwIVyyNuW7dYY4yVbqvDbH+Rh4boK+CUKicrhI0qB+ZNzAMBg7Hki7+6dAodFGyrRo5tgWc6q3Msl8PBJsiNR5CPMLKR+520WdhElaqjOcae48Ktrh+H5zeF7IQCgrOY0flw00FBJJDsjB5w8p5k9oa/yCVeeqkiP6D9iKMfl8VONA6o51QKXwMFn0lcRCpcgN83trK5nvQ0MRivp1I4BsC5rzMtIxe5HJ6DmVAv2HG7A85ur4BQIFm2oxLfNXsM1IgVEUYIS7g9VzurxUzx243AUDXEjMy0JFXVnwOn1uCHPS5BADTtv42hMUTMaU83tRVlYuq065DAhhTf31aD+rAfPTM2PWrYiVHlqRV1jVOW0HBB0QHbnWCsIHHBbYRZ+OnoQ0pKdGP3MVtbbwGC0kk6ZY1ATyuAphuiFLVXw+uWSzfM+CUu3fY5zXnPZbQX1blo5ZahZsqUqOC965vISw+wGl8DhT3eOtBxUrx2NSYKjMfUlou4UF34zxfj+Vmw59A2Knt5iGMWprwpy8kQTMrNHhNnwAOd8UnA9ZuXBVwxOg8Pkv1SeADzH4d2yOkxZuhN/33vUVqEAg8EITad3DKF0epTOaH1zlxDIK4RCvZvOy0hFiktbscMTgoq6Rsx764BhN+8SCBZPHYFxw/qENbzyaMzQJaJ5Gammmk1W+CXg8bfLsXKPcU7z/Mk58EkXQmZ6RxRKyyg3vSeiqKIFIFcUVZ84a1oe/GlNI54oztPcW+AAjtOWzy7d9rmhEoz1NjAYkRP3UBIhhAdQAqCWUjqFEPIdAP8EMAjAVwCmUUpPxXMNZrFxdWe0vhzU6xdNE9MK6nJWAKbhj2aviN1fNBhCUt2dPP50x2UYNyx8T4Hd7t5kJ28Yl2mHJ94t10hcNDR55FyIX4JyYFKHYsJpE7lTXFgyrQBz1pTBL1LbSXGF0mOncXG/Hpahv72PXRscMQrI1VNq2XMnz2PWuCF45cNqJsXNYLSCtjgx/BLAQdXPjwD4gFJ6MYAPAj/HHaXsUjkpmHVGJ7vkctC7x1qXgSY5uGA5q/reZuGk13d9ZSh1Vaqe7GA37i/PO4g8jOOTgNd2HA7+HKpnw6xzec6aMsPJobggA7+ZkgueI+ju4ODggGu/2xcugQRDVLPGDjZdzyB397Chv3HD+mDcsL6W5bO3F2UFey+swnQMBiM0cXUMhJBMAJMBvKZ6+GYAfwv8/W8AfhDPNZhhZgCTnXI56K5543H32CHgLb4ZUTI37GbhJCfPYfY1F9uSmzbDKgwGwCAPQWxoHZnxx/8cDsb3Qxlls+/M46f4+16t8F7w1CFSnPNJ8EnAzup6bLhvLP4+cxR2PzoBIzKNI0oB4INDJ2UnOyUHToELOmqz7yxUiFC/CWAy3gxGZMQ7lPQHAHMB9FA91o9S+jUAUEq/JoSYxlQIIbMAzAKArKysmC7KtByUastBnyzOw+PvlBuu/dkY892uWThJ2cHeXpQVdV29Pgy2s7oeo5/Zagjn6KuYJCqX19ph4bpKTMztr6mG4jkCn6id5azuq1BYuq0atxdlaZRT9aEgjhDUNbZg3LC+WFtai1+9WWq6jj//5zAy07oHpMEJfH4JC27Ktdz1hyufZbLcDEZ0xO3EQAiZAuAkpXR/NNdTSpdRSgsppYV9+vQJf0EE2BkcM33UQDw2aTgEniDJwYEn8qyGFXuOWgrI3Xt1NlxCdKeDcOtV5kNYCdHp5SsiOT84eDlRXnbsNEZn95YT0H55lvOT6yrw8gef4639NTDT8BM4oqn6MXO657wiZi4vwcq9RzB3TZll/kYCsHBdRTDE5xUpFm2oNOz2q0+cxZqSY6g+cdbyM1WfOIs5q8ssRfsYDIY18TwxjAZQTAiZBKAbgJ6EkBUAThBCLgqcFi4CEF2bayuxs9tcsrlK7pYOzFnwidS0K1i9MwUoZo0bEtxFry2txdw1ZeAJB5FKmi7fSAmXjFb+VJ84G1Ey2uuXcPffSiBRaE49Sn4kVANds1dEeV1j0HEpTnfOGm01lsdPsXBdJXhopb/16NVEqESDjqei7gxe3f4FdlQ3BJ+Xcxm85kSwtrQWc9YcMHwHTJabwbBH3BwDpfRRAI8CACHkagAPU0rvIIQsBvATAL8P/O/aeK0hHFbNWupEqxXq+nh9l/QrH8rhFaUTWLaPspF94J+l6NXdidz0nrYMlFreIZJktJUMhRl2Q05WLHy3AkWDvoPsfnLEUJ5P4cQ9b+zX9G84eIIWj3VHMwHg04XjPCLF1oMn8MqH1abNc6JEcdZzwVnnXNQT8946oJmop8BKVxkMe7RHH8PvAVxHCPkcwHWBnzsUdmS7fZKEZCePbYdOGr5ExWlU1J0xGDORAj9fXmJrnoF+1vSu6vqQITAl0Vp/9nzEpaKtwStSTHp5p+bz5Kb3hKQ7GfhFCquvVeCAB68bhm4mnWwvbjV3CnocHIf3K46DmrzWyRNWuspg2ITQCJRA24vCwkJaUlLSZu/X0OQJSisoOHgCjsi18j5JwrSRmVi1vwZUovDorHA3B4dd88ajoq4RM17fZ/k+yuusTi36NSivB2AIganDWef9YqtPAdHgEgg+emRCcH3ldY1YtP7CjOl7r87Gsu2Hgzt85TM9MvES3JQvh9f0nzkSrIJUDp7ghWkFuGKomzkGRpeCELKfUloY6XWdXispGqyURJWcRLKTx5SlOy0NmCIlkZueCgdPLI10qJh3qHyCUoqpYEfhtS1QjwpVvrf5k3OQl5EaDOG88mG15hpJohiTfaEDfNrITCzfc9Rwbys4AMkuAR6/3zQ5rpw/Hv3Xf1llEoNhk04viREt6iofpVFKqQ5q9oqWoaZkF4+8DLnPwZ3iwvO35sMlcEgyCZGEinlHMrrStDEtioa3WKCMClUqgRauq0Cyk9dId6un3HEcwZSlchjKTA4jFC6Bw1/vuhwr7i7CY5O+a/oawsk5FFaZxGDYhzmGELhTXME8gro0MtSEOFGiwZkASvnnR4+Mx5uzrsBTP8iz3ewWrqRW3bhltp6OEiJU8g/KzObR2b2xfvaY4JS78z4paLAr6s6YOlwnT/DYpOHQ+zpRkpCbnor8Ab2QZ9FN7tQ1/jFRPQYjPCyUZEFDkwfz1pRhy6Fvgo9NurQfFt18KQC5Z2Hpts8BKlfOKEnT+ZNz8NIHn2PFniNwcASUIFiimj+gFybm9Q85z0D9nFVJrVnjlr7BzefvGI4BkMthH3+nHCkuHn6Jyv0ePKepHFJKffUOzilw2HjfGKQlO7H4/c8gqsJyRDUL2yHwhkoss8osVpnEYISHOQYTzKaQAcDG/57ApoqTACiSHAIAgtnjs3FjXn+5nr+2Eb9+pzyYAFWM2IOrSjWT0MxOCVZduvrXm+UT5r51ALvmjceueeNRc6oFjS1e/PSv+9CO6QZTlDGlL2+thj5N7Avs/pUeCJ4jECWKxVNHILtfD5QdO41uAq8Rzesm8Jo5Dg6Bg6guGBA4/HzsEPzvf76Ag+cgUePsCwaDYYSFknQ0NHlMnYKCX6LwS8BZjx8ev4RXPqxGWrITmWlJWLiu3LQqxi8B68rqgrFtfeeumUCdVSw8lNCdej4zifLAEIl8d7R4RcmQkJ9WmAl3iivw/dGA37jwGqucS7KTR9mx0wCAe8YNAU8IeCJXKLk4ghe3VsMnUpzziijOv4glnhkMG7ATg46KujMR9QCoY9ahplE+vfEgfvfeQRQM6IW9X15QGZ9xRRZuuWyALXltwNxAekVteMSd4sKS2wpwv4UmkRUugbM1CS4W6L/if3x8DDNGDQrMr6BQGgLVHeb6SrFphZmYsnSnqXQ6ADTqmulWldRi1tihwUY8BoNhDjsxGIhsq63ErDPTkkJGbrwihcdPNU4BAJbvPgqfX7RdgaQYSHXVkShJ2FVdr3ldcUEG9v/6WhRm2ZP4BtBmTsEMn0jx1IZKw9AkB8ehou5MMHGtVIqtnz0Gq0pqDNLp4fjthgpWlcRghIE5Bh1K74Ed1N207hQX7rZQXg3HVw3nDOMsldCKGaOze0NtP/0SLENP5V9bC811NLZV1QfzEAotPj9mLi/RdH8rJcM8iTzs9WFVA0b+dkvYrnMGoyvDHIMOpfcg3IhKp8Bh4/1jMTq7d7Bs9J6rh0Y19XiQuztW6uYarNh9NGjo9TMFak61wMlrZz+YlWHWnGox7MAThWQXD5dAQAgxHW2amZYEnxj9Ceeh1cYhQwwGQ4blGEwoLshAzkU9MfHFHYYZC4Cs6/Pc1BGo+PqMppLo3quzkeISNJIP4RiZlYoPDp005DUkAHf/dR+mFg4IzCfQdmDrJ8MpoSd1yWt5baNhB54IdHdwmHP9JQAonttUBZ/qs6q7vxfclGs6M8MOEgVTWmUwLGBaSSF4t7QWD68uM8g3uwQOG+4bY5DFcAlyLb4njj0E3Rwc5k/OwRPrKoKVPQIHLJlWAAoEHVWkw3o6EhzkUlOBMyaV9fpSK/cewcJ1leAIItJYcvAEex6dwBwDo1MTrVYSCyWFoLggA6/9pBDdncaRnaXHThvKRp08hx9fHttpc3p8PknjFAB5QpoiN62UvHr8NCGdAgAQIifCzeZxGwYqFQ3E7kfGY9mdhQHHbI/nb81nToHBsIA5hjDkpqdCosZmrIIBvQyVRC0+P5bvPhLX9YgwngK8IsXru74MKxWeKOgPsep53GZ9CO4UF8YN64PZ12Tbun8PJ4ffrq/En7d9HovlMhidjs5hSeKIWrMo2cnDyRPMn5yD7H498OwtI+ASOHQPPA60X7Pxmv01hryDwCGiXXRHQf8d6udxm9HQ5EH+gF5hiwYA4KxXwskmL373fhW+++uNrVssg9EJSTyr0Q4UF2TIc5AlCqfAYdGGSrxbWqvp0qWg4KIon4wVXpHi2uH9NKJ7S6YV4NUZhXAkaGWSwvwpOabigQrKQKN7V35iOG2Eo8VP2cmBwdDBqpJs0NDkwaINlfD6JXgDBb6GRGoAACAASURBVEcPry4DBYU8uVLZqRutkpMnWFCciyfeLYcvzgVCWw6dxIb7xqDZKwZF90q+bDCMywyFwAE8IYbhQ+3Jt01eAOZ6UqOze7d6FsWb+2vw82sujuWSGYyEhjkGG5gNzdFXKgGAiyfwSzRYeurgCZ67VVZWLRr0Hdz40o64JoSdPIdmr4j8Ab0AyIZ0zuoyw+tCz4PuWE4BAJZsrsKnR0/hP599AxHQiAcuu3Ok4d+mm4ODT5Rgt80hpz+TyGAw1DDHYINQ8xfUEI7g/fvHoq7xPACK3PTUYAgku18PPH9rPn75ZmmEohv28YpiUEZDEeYzc2Ch7L5Z30Z7QwFs/ewbw+Nysp0YcisA8M+Zo/Dj1/bacsQPXHdJDFbJYHQeWI7BBuoEdHcHb/oagUMwKT1uWB+MG9bXEBfPuagnnHFMBt81enDwPSvqGk1zHg6eRNWd3RHxiiKOfXsOal8mcMCzt4xA4WA37h8fPjxUkJmK7H49THMXDEZXhZ0YbKIMzamoO4OZy0sMgnMugceiDZXo0U3QlFSq4+IeUQKJY0PhFUPdwfecu6bM0Gjn5AlGD3VjW1W92eUJx5RL0/Hk+krNqYDnOIzO7g0AuL0oCy9vrYY3REzp4PEzWLnniKG7nMlzM7oy7MQQAUq9/OKpF8pXFZq9omGOgn7OgtcvxTV+/22TJ/ieeqfgEjg8fP0lncYpAMDG8q8NDtrJX9CMcqe4sKA4J+Q9BJ7DwnUVtmZhMBhdBXZiiALl9LDt0Ek8sa5Co0eknqNglrTu5uAgSRRybDy2XQ+/WnUA1363r+E9AeCu0YPwnWRnTN+vvWkxkcDwiJLGYU8vGghQYOG6CvAcMVzjEykcPKfJU1jNwgCM41cZjM4IOzFEiTvFhWuG9zUka9VzFDLTktDi0wrq+UUJG+8fi5+NGRSXdW05eBJNJiJ+r+380lbzV0dnyqX9keIy5nmcvPzhCKWYsnSnRlZ7+qiBWHBTLkSJwiXIGRYXT9DNwWHBTTkQTTrb1bMwlPzDyj1HMPqZrUEJcCbdzeissBNDKzCbKqbX8pEH1hsH2L+643Dc1mUWrPKJFA+/9V8Qi+cThfX/PW6Yl+HkCWjghOQRKSBSPLy6DDkX9QwmlhdtqNRUaFFCsGH2GGT364EeLsHy31DJEamnxKnLZZXpcgxGZ4I5hlaihJXMwgs1p1pMB9j/8T/VaI9haf4O1p8QLfoS1Ou+2w/bP6/XfM9ekWLSyzvx3NQRGOhONoTXXIGeD8D631CdIzIjVMiJwUhkOkFwof1xp7iQP6CXzfnMIt759Ou2XF6nZ/PBE6a9DF6/nEhOdvJhR6ea/RsqOSIrrMavMhiJDnMMcUTd/6DoF901erAhps1oHYpOlFkOxcHJJwP9v4M+5GeGVWOjlQQ4g9FZYKGkOKMPU1TUncEf/xO//EJXZUP5cdPHlV19/oBeliE/K8xySPMn5yAvIzV4D1alxOiMMMfQBrhTXEGjkZveM4xWESNWuIQLu/poDXioHJKZqB9rjGN0BlgoqY1xp7jw5M157b2MiOETUEfj0RuHo7ggIyjLHW2ZqVn+Qd+8yBrjGJ0J5hjagemjBuKpH+YhkcYkJOIJJy+9J7ZXfYO5a2JvwM0S00qVEoOR6LBQUjsxvWigLMX94nZEMMOeEQHT//KxPFtCVxscizJTs8Q0q1JidBbidmIghHQjhHxMCCkjhFQQQhYGHi8ghOwhhJQSQkoIId+P1xo6Otn9euD5aQWyaqvTXLWVET0ev4RzJtORYmHAzSrOQlUpMfVWRiIRzxODB8B4SmkTIcQBYCch5D0ATwJYSCl9jxAyCcCzAK6O4zo6NMUFGTh73o8F6yraeykdEoEjMZsR0d3JQ6I0ojLTUEnrUIlpNSxJzUg04uYYKKUUQFPgR0fgDw386Rl4PBVAXbzWkAgocg2dpSu5NfAcDFPXCInN9+ISCP50x2Wa4UnhsGPQ1RVnZqiT1ExKg5Eo2AolEUKGEUI+IISUB34eQQj5tY3reEJIKYCTADZTSvcC+BWAxYSQYwCeA/CoxbWzAqGmkm++MU7v6iyE667tKvAcgSMghOdUlUDFak72bZcPCA5PshPWiVXVEUtSMxIRuxbpVcgG3AcAlNIDAH4U7iJKqUgpLQCQCeD7hJA8AL8A8ACldACABwD8xeLaZZTSQkppYZ8+fWwuM/GwOza0syNKNKhJJFGK7o7YOstVJTVoaPLYLl2NhUFvaPKgscVrkOtgSWpGR8fub193SunHuseM2s4WUEpPA/gQwEQAPwHwr8BTqwF02eQzYJ7EfOqHeVh+1/dxR9GA9l5eu+CXYBCucwkcfvfDPAhRlvg6OA4VdWdsnwLMda7sG/S1pbW48vcf4J43PoEoUQgcIpLjYDDaE7s5hnpCyFAEFJsJIVMBhFSCI4T0AeCjlJ4mhCQBuBbAM5BzCldBdhTjAXwe3dI7D1ZJzDMtPqzYe6ydV9c+qE2ywAGLp8rx/YNfn8HyPUcjvp9XlHD4myYIuuYRq9JVxWE/tLosqOYqShJ2VdeHTRw3NHnw0KrSgIKufFrgCfDK9MuQm96TOQVGh8euY7gXwDIAwwkhtQC+BHBHmGsuAvA3QggP+WSyilK6nhByGsCLhBABwHkAs6JbeufCLIl5xVB3ws9PiAWEEORc1BPVJ87izZKaiK/niGzUF7//WVBqW8EnyRPfyo6dNlQWjc7urWlC9Ev2EscVdWcMsupKbUG0ToFpMjHaEluOgVJ6GMC1hJBkAByl9KyNaw4A+J7J4zsBjIx0oV0Rd4oLL/6oAA+vLgVA4BcpumI2widSTHxxByillh3YPAFy03vgQK3Jf5oU8FPAr3IKyS4eokQxrTATU5buNFQeNTR5sO3QSQgcBw/sjf3UvKEJc9eU4rrv9sevrhsWkXFn5a6MtiakYyCEPGjxOACAUrokDmtiqNCHmZ57/xD+sS/yXXOiY9XLwHMEdxRl4c19R/FlfYvpCUvvTJOdPBbelIuCAb0w+eUd8PipppT07Hk/Fm2ohMAR0xNGuDxDbnoqHDwxDBQ6fsaLN/YexRt7j+KlHxUEjXuo0wArd2W0B+FODD0C/3sJgMsBvBv4+SYA2+O1KIaWcLXyXRpK8ea+o/D4KTx+e/UQXlHCNcP7YuVe+To1PCFYuL4SXl0sKNnJQ7TZHOdOceH5W/Px8OoyzThRNb98sxSjs3tjZ3V9yNOAUh2lnj7HJscx4k1Ix0ApVWQsNgG4TAkhEUKegFxRxGhj9LvQro7LwQeOCPYbHiSJYlPFcby81Vj34BVFuAQeXpWPSXbJJ4xrhve1bYyLCzLQq7sDM17fZ/o8BVBR1xj2NMA0mRjtgd1y1SwAXtXPXgCDYr4aRlgKB6a19xJM6Z0smE5Qizc+UYJIQ2deXDrNcJECj75dbupkrxjc27DLFyUakVNQyE1PhVV1LQn8/3C9EpFqMjEYscDur/IbAD4mhDxBCFkAYC+A5fFbFsOKwkHfadP3y+2fjMduHB40TE5elq5Q4xI4PHDdcPDt0MFNKcVvpuRaChF2d3ARJey3V9dDCuzQXQIHp8Bh/pScqAyxUjxgxos/KkBuek/T04BSJaX0VxQXZGD97DF46PqLsWBKDnp1dzAxPkZcsVuV9FRAAG9s4KGfUko/jd+yGFY0e0U4+NhJRYTjyuy+mHXVUNwyMhMr9x7FK9uq4eDk8k8XT0A4gvmTc7BoQ6VB3rotcAk88jJSsX72GOys/gZPbzwIdb5YAjC9KAt//eiI7XsqH8Pjl8ADWPhuBb5t8uL2oqyIHYRSPPCHzVX46IsGXDnUralK0o8ONauSogAeVvVTAHJvx5Jp9hLYDEakEGpjMD0hJMvscUpp5J1GUVBYWEhLSkra4q06PA1NHlz5+w8MSdN4MmpwGl6ZPhKjn9mq6Uh2Chw23jcGzV4Rd7y2F2c9tpvhY0rhwFR8cqQRPAdIFCAESHIIsqEdmYl/ltTExGk5OOCJ4jxMHzUwBqu+gGLUk508pizdqfmOXQIBYJwpAcj9GfsevzZsApvRdSGE7KeUFkZ6nd2z/wYA6wN/PgBwGMB7kb4Zo/W4U1xYPDW/TeP5e748hc0Vxw3xcBfPodkrIjMtyaAH1JaUHGmEBMAnyfkDvyR3Ga+fPQar9sfGKSBw/8ffKcfKveanj2hnLiijQ5u9ouE75gkHaqGlJVFgU8VxNmKUEXNsmRdK6aWU0hGBPxdD1jfaGd+lMawoLsjA3seuxfK7vo+xQ91t8p5v7jtiKQbnTnFh9jUXG65x8pxl8jXebPxvnamhBQBHKwdYL3i3AtUntI10rZ0rDZhXIIlUMnRRqzlc38zUWxkxJ6p9J6X0E8h9DYx2wp3iwrhhffDGzFHY8sA4tNLWhaW05qymWkfgoKmOub0oCy7dMYbjgNU/HwWnPlvdBry5rwZ7DzcYDK1L4ACL8Knd79AvUkx6eWfQ+NuV6A53ojCrQPrNlNyQaxnaO5mptzJijq3ks64DmgNwGYDOOyQhwUhLdoLjCMQ49zio7SkhBKOzewd/lkNc2kTqs7eMQOFgNxYU5+Dxt8vD3p/nCG69LAOrSmpiIv2xeNNnmHPDJViyuSq4pnuvzsay7YfhM8mHRPL1ef0S5qw5gF7dnTjT4oP+bKRvQrMra6HvdK851YJkl2Cav+E5gqc2HoJEZUet5FVYOSujtdgV0euh+rsfcs7hrdgvhxENNada0E3g4RPbLvnrEykq6hoxblhfAPJueKA7GetnjwnmHRTjlJeeimQnh2ZvaHMvShSEAJseGIfXd32Jv3/cOmVZn0jx3Puf4eHrL8F3kp0oGNALaclOLN0WG0Ffj1/C///6x6ZOTL1rj1TWQt/prj/1cAAEnoNXlIIOwyVwTL2VETPsOoZKSqmm05kQcitY93OHoL2G/Ww88DXGDetruhvOH9BLsz67c5v/sa8G/9hXE7PQmFekePq9Q3DxAAjBbYUDEKMR0gCMOkyAXEmk3rW3RtZCCS+pT2LKqcermoPq5DmkJjmYU2DEBLvBX7Pxm6YjORltjz427RJIqxOsdnizpAbLtn8RNr5ulZwOhVVY57Ebh+Oeq4ZEvFaPCHj8FMv3HA0pK9Lary3JweHVGYWaMFFrZS2KCzKwa954rLi7CLvmjcftRVlR5RWirZpidD3CqaveCGASgAxCyEuqp3oigglujPijjk03tnhx78pP2yS09My/DyHJoe045gjRhJkAOTn9hy1VEcXxzXj6vUNxLdVtrTsVJYrc9FTNY2a7/kjzAOrw0trSWs2pR18IYAaT7mZEQrhQUh2AEgDFAParHj8LeV4zowOhGI+GJk+bhZZECWj2aHev57wiZi4vweKp+RrjE6sEeTwbrFvbN7jgplxTA201pS9SlHyF+tTDc5ymEMDqGibdzbBLOHXVMgBlhJCVlFJ2QkgQ1DtUDgTn4qyfYWZLPX6qMT5mCfLuDh6AhHO+xFSM5XAhx8ATYNZVQzAxr7/l62Mhn26Wr3DyofMVTLqbESkhD+WEkFWBv35KCDmg/9MG62NEiRKX/tOdIw39BW2FutHKLM4ugWLHvAn4bXEOBru7o2+KEwRRNte0A+pPI1Lgjd1Hwja3tTbOH02+gkl3MyIlpFYSIeQiSunXhBBTcRhKqX1lslbAtJJax7ultfLpgRCc87addEU3B4dd88YHd6Ur9xzBwnUVcPBccOiNPs5dfeIsJr20w3LATSKg/9wKsYrzK/+ekdwnmmsYiU+0WknhQklfB/76P5TSebo3fAbAPONVjI6GEt+uqGvEzOUlbSbAN39yjiZhumhDJZwCB69IseCmHFPD1OwNDMrRJc5/VJiJNZ/U2i57jScEcnOZ1VqoRFFRdwapSY5gPqGhyYO5a8oMY0SjifNHk6+IVY6D0TWwe2q/zuSxG2O5EEZ8kSU0+mLx1Pzg7AKOtL4KxwonT5CXIVfnqJOfTR4RXr+EResrTcMpsiCfMbv8dmktFt6cC669xJdUUFjPoAYAj0gxc3mJRjfJbIxoazSNFOE9xcDrQ1RmISv9NQyGFeHKVX8B4H8ADNHlFHoA2BXPhTHig3rnWF7biMffCS9VEQ2EIBjDjiT5Kfc8ZOP5zVWax508j7z0VOx7/Fr8+u0DeK/iZFzWHSs8fimo6jpnTRnMXLBXFA1x/mjmKuhDVNMKM7GqpIaFjRhRE65c9e+Q5bV/B+AR1eNnKaXfxm1VjLiiGJybX2mdbxeIvHuWqLYySeCAxVPzo55bfHtRFpZu+1yzw1a//p6rL4ZD4PFu2dem13c0eMKZHs1mX3OxxvhHk4MwK0Vdvlsek8JKUxnREjKURCltpJR+RSn9cSDR3ALZBqRYDe9hJAb/+qSm1ffwU7kaRzHfDh5Y+uMC7H3sWo1BM1MNnT85BzWnWkzDScrMCf2c453V9UFp602VJ/DYpOG45bIMGAd6diw8fhGiLvTkEjjcXnThV8iuQqse5TQWCibDzYgUu+qqNwFYAiAdwEkAAwEcBBBaE5jRYdl/5FTM70kl4J3SOiQ5eEzI0dbz60NYizZUhtwZ65OlAIIT5JSd8JLNVVg/ewzWH6iD2IYT7aLh5oJ0rCq54IxvuzxTs4OPttfAjk4WK01lRIrd5PNvAYwCUEUpHQxgAliOIaEZOTAt5vf0U2DLwZP42fL9uOGFDw3Pu1NcyExLwqINlbZ2xupkqdnO2MHJE+QWT80PqXHk4AnW/HxU3GdWWCFSYHWJ9oT2z33HsL3qZPBzR9trYHYam3FFluG01ZZhJKbJlPjYVVf1UUobCCEcIYSjlG4LlKsyEpT/77JMPLXxUNzu/9mJZvxuQyVmXTU0bjtjxXDmD+iFXt0d+Pkbn6BF1+XNE+CJ4lyc84kgBOZt2m2A/m09fop7VnwCSdXPEa2eklkp6i8nDGuX0lSmydQ5sOsYThNCUgBsB7CSEHISTEQvoXGnuPDQdcMM1T+x5M87vsSyHV/ixR8VBI1Da3fGVoYzNz0Vkq5Z08lzePj6YVi0vhIcIXHVWIoGpdlQSQ63ptdAL7cRC/mNSGGaTJ0Hu6GkmyEnnh8A8G8AXwC4KV6LYrQN8jhOY3xlkDu0keY52O4noAAeXl2qCSvce3U2XELkoQ69/LR6J7qzuh6iyuE4eIIFxTlYsqUK531Sm3Z8RwpPCLYdksNKidxrYBXuY4nvxMPWiYFS2qz68W9xWgujjVGqf+a+dQCSKMEryUNmjp/x4LEbh8MnSvjDB58b5hc8WZyHiXn9saniOOavrQjbjewVgXtW7MfwfilY/UltwHhQzBo3BLcXZUW1M1bi2MpJY95bBzQnAo4AA9K6J4TuUrNXxBPrKvDrteVhQy/R9Dm0FUyTqfMQTivpLMyjsgQApZT2jNfC1DCtpPhSfeIsJr28E16VZVX0fnZV1wd0luRpaHNvuASzxg0Nvu7d0lrMWVMGnnARq7haaQqFQx3Hbvb4cVFqN3xz5jy8qv9Se7gEFA35DrYc7LiNcEkChxZdfCvUd5II8XumydSxiJdWUo9QzzM6B81eES6e0zgGJQRQXJCBs+f9WLi+Et0EDks2V6F/z27BX3bZFsvaGs7AHGK7RCP9bBbHrm08b3idV5RMnYJaKru9uWvMILy+6yu0+MwT8erTAYC4xe9jeQphmkydA7vJZ0YnJlQIoKHJg0UbKuH1S/AGyg0UgwTIxsqjcig8RwzNXFbowwx2DJRZVZMaJwE4gcPkS/vjrU/qDM+TDuIZCIBXd3xpUJFVvhP96eDeq7PjMlMhHqeQ9kh8M2JL3EKwhJBuhJCPCSFlhJAKQshC1XP3EUI+Czz+bLzWwLCHWS28khAOlVA0e667g8ec64ehW4gZEN0EzpB0XltaG+xqDjXTIFxDV6a7O3bNG48eLofp8xEcaOIKBQxOwcHLirQADF3QS7d9bjiNtTZ+H223dUeA9UrEl3ieGDwAxlNKmwghDgA7CSHvAUiCXOU0glLqIYT0DXkXRptgFQIIl1DUP+fxiyga/B28bFG11DfFgeemfQ/AhdnIkZQ5Kk7s/jdLTe+fn9kLAPDX3eFHhXSQw0MQgeOwaEMlvm32mkxp4zFr3BC88mF11HOj9STqZLdEyLUkOnFzDFTOajcFfnQE/lAAvwDwe0qpJ/C6jpsd7GKYhQDC9Q8oz1GJwiNScBzBHa9/rFH4PO8XcUm/FNw9ZjAIx2HWGyWaew10J9s2UA1NHgx0J+MvM0biZ8v3Q8+912RjU8VxW5+X5wgEQgw7cSdP2mVQkJJrWLqtGvqaD58k4faiLNxelBWz+H0iVhGxXom2Ia45BkIID2A/gGwAr1BK9xJChgEYSwh5CsB5AA9TSveZXDsLwCwAyMpien3tSaiEYnFBBnIu6olJL+8EQHE+YNxWldRg/ewxaPaKmmE1er2juW8dwPrZY2wZKP1OcWy2GzuqG4LPz7giC9n9euCfJUdtfS4HL6ueqlscujt5/OmOy3Ds25a4SZKHw8lzIU8HsTKA4Zx+RyRRTzmJRlwdA6VUBFBACOkF4G1CSF7gPdMgay9dDmAVIWQI1dXNUkqXAVgGyOWq8VwnIzyhEopWVU3NXhH5A3oFH7P6pW72imENlNlOcd+RU1jz81H4quEcCgb0QnY/uYjuhpz+eHXHV2E/k18SQYg2FyJROcQ1blhfgACPv33BOQghprbFknicDqxItCqiRDzlJCJtUpVEKT1NCPkQwEQANQD+FXAEHxNCJAC9AXzTFmthxB67v6xmr/P4RSQ7eVM1VaWBTZ0E1zsVh8BjauEAzT0H90kBR+Q5EaG4b/wwDHR3t3RI04sGYmJuf1TUnQmMRI1PRkLgoGnOm1aYaTgdxLOxLZGqiBLxlJOIxM0xEEL6QBbfO00ISQJwLYBnIOcdxgP4MBBWcgKoj9c6GPHH7i+r+nXqnMSUpTuDCUR3iss0uTg6u7fBqZhNQAPkk0myU8BZj7WclzIPwZ3iCrljdqe4kJrkgJPn4uYY9LddVVKDGaMGBcNw/y4/joXrKuDgOYgB0b1od/l6B9ORO6mtSLRTTiISsvO5VTcmZARk+QwecgHIKkrpk4QQJ4DXARQA8ELOMWwNdS/W+ZwY2DUyZp3WLoHDR4+MB3Bh7oKCugv7wVWlQUPq4AmevzXfUJGizmUoCBzAcxycfOSVLGb3c/AEokhtVzU5eGKQFkl28vBJFITKTlL9eSWJwiXwOO8XDdc5eAKOyJVKVp/F7N+CjQDtesSl87k1UEoPAPieyeNeAHfE630Z7YfdkIRZTsLjl/D3vUcxblgf8ERb60olioq6Mxid3Rs8x8EfODn4RGpakWJ1ggm1y1QMabKT1yTMQ93vSMM5W+q0Tp7DfeOzg8lkryjhrtGDcMVQN9JTkzBl6U55aEMAxQF5RfMTj+IoPH75ef13YHXiCjcC9P43S7H/q29x/7XD2C68ixO3E0MsYSeGxKahyYOKujNQ9y5c+futhtCMSyB48LpL8Lv3jHMiCIB7rhqCFXuOakJEPVwCVtxdhMy0JIPRt3uCUQwpIBtlF09AOGLYQevv98L7B/HitsNhP7/6NLRy71G8su1zzW4fQNDpeETJcIIIe3+eYNU9VyJ/QC/T0003B4dld47EvSs/DRleU/OSSiqdkbh0uBMDgwEAK/ccwW/Wlgc3xEr4Z/Y12YbdtsBxePbf5sODKIA//uewYQqbT5JQXtuI25btNoRE7Jxg1NVOCh5RHmat34mr73fJ4xvgsaEZ6OAJFk8dEYzn/++H1fD4qWa3v2veeOyaNz54YtGfIMLhESmSnfLka6skPWDs1wjFg/8stewNSIS8RCKssSOTCKrEjARl5Z4jePydco2N84kUc9YcwI15/Q2zIM77xLD2kEI+WSjSHfMn59geFWqGmayHgtUsgftW7rflFFwCwV9+cnlw511zqgWCbpCFugY/P1ByO39yDpw8gcPmb6dL4FAakIewqhDLTe+J2ddk27shZL9k9tntSpe0J4mwxo4OcwyMuNDQ5MET6ypMn+MIUNfYgrtGD4aDk5OwLoGAszH9R6LAXaMHB4f15GWktmo4TCjtJbOS24YmD9b9115ntV8Enn2vErkLNmDkk5vwh02foUnnUfTvsba0Fos2VMIpcACxNw3J45ew4N0KjH5mK3ZV11vqXt2Y19/W/QDZMJh99nhqK8VC/yiR9Z86EiyUxIgLyu5YX1EDyOJxd/11X7C6yOcVkZ7qQn2T19a9X9/1Fe4eOyQYImhNw5M6sQwYcwz6Jrt1ZUbFVitESlH+tawK0wwftn1urMqePyVHkxPRh7V4QiCa5AF5AnRz8mgOOJpm1ZhQdWhKHUpp9oro5uA097fiyR/kGUIw8ew6jpX+EeuMjg3MMTDiQmZaknWDGaXw656ra7S/o+MJQUXdGYwb1icmDU/qunizqiTgguHyx7iX4VuVMzQzat2dPG65LAMr9h4FzxFQCiwozsHE3P7YdugkFrxbEXQKwAUjaDYe1K6zfGzScEwfNdDweLy6jmOpf8Q6o2MDCyUx4oI8NnQEHKpsMQfgF1cNkcMkreCcT8TM5SXB2HGoWdCRrFeJ8euNqtpw6R1aa3l+c1UwzGFl1O6bcDH2PjYBq35+BXY/Oh7TiwbCneLCNcP7Gk4ToYygO8WFaSMzNY+NzXbDJXBwCRwcPMFTP8zTTOjTX28VpmoNsZwVHa81djXYiYERN5SdeEVdIwCC3HR5Euxfdn7V6nt7/JJmVxlPWQezpHEs2VRxHD8OGPtQpx+zMtxITksNTR6s2l+jeWzfkVPYcN8Y01OSGfHoOo71Lj/UGlm1kj2YY2DEFXeKSxakU7GgOEcjThct0caOIzUO5bWNhqSxgksgmFGUhVd3hZ//YMWjb5fj4PEzePLmSzE6uzeW3TkSiiPVr88s7CcHtgAAH8ZJREFUFm+WTzDDLFRFKFDXeB7jhvWxvd5YO+F46B+ZrZHNcbAPcwyMNmd60UA0n/fjaZNGtkhQBPgiIVLjoIw2teK2ywfg8ZvycOmANDy0usw02W6H5buPIrNXdyzZUmXoWFaaA9NTk0xj8bvmjdeo2OrXrzgNs515i1/CT//vY7xwW/s2tMVb/4jNcYgM1vnMaDdW7j3SqpODgyfgA9VDdoyKVVfwrnnjLa8pO3Yad7y217JjWH199YmzuPGl7fDZ6HEwQz9RzsETSBLV9HYQaEf4KJ3fZo5h5Z4jeGJdBQgIKCieKM4FKEznTLgEgo8emWDLSCZiOMbs3zHUd9dZYJ3PjIRjetFADEhLwozXDXOabOETKXwixUOry8KKygHRlTJmpiWhxWstI8EF7utOccnloIIAn4XGUTh4DlBv6M1OH/pHlFh8Q5MHmyqO43B9M27I6YfPTjSpHIB81eNvl+MXVw1BkoMLTosLvjexF5ZL1HAMq1aKDOYYGO1Kbnqq7dp6K8KJyilEYxzcKS7kpvdEWe0Z0+dbfBeuD9UsZweb/WxBXIJ8WtpZXY9fvVkadBqv7vjS8po//eewplJM4bxfxMG6xrAnrkQNx7A5DpHBylUZ7Yq+vDDClIEpVqWO0ZYyjhyYZvmcegev3N8lEHR38HDyBA9dNwxbHhiHOdcPg5O3/nWbcUUWFtyUF/azKfAEeHVGIUZn98ac1WWGk0So9fpNTiISBR55uxzff/oDSwkJO2WlobqXY9HZ3BpiUdbcVWAnBka7o088Pvb2AbxfcTLq+4U6BUST5Ly9aCBe/8i86ihJ0IZgZJNL5P8jBAPd3ZHdrwfSkp14eVs1oMo/CBzwwLUX44bci5Ddrwe2V9kfYsgRID01CdsOnbTtFBRCnWlESdaysnvi8ooijn3bjMYWL45924In11eCENn5XHNJH9xckIErhrqxs7q+Q4SgQlVUJWLuJF4wx8DoEKh/Yf985+Uofnk7DtSejfg+Sngl1C92pOWW2f16BIfa6JFAg05ICbWo5cTVoRazUIbWOJqbeHeyAw3NPs1j1+X0x+SXd0DguKgroUKhODu9sVR/hhafHz6RYvY/Sk3vsfngSWw+eBI8ISCEwi9dmP0wZ80B9OruNC3JbQ8D3Za5k0RwQMwxMDoki34wArf9+SOcj7DV+MeXZ8XlF/rZqfnI7pOC3793KLjjFjhg8dT84C93uOR2spPHDTn9cGlGKn54WabBKOSmpxomvTl4gk0PXIXSo6ew7sDXAKXYWH4cG8tlIT8PzEugUrvxmDvxu/j1O+VBdyM36VHDKFEzMtOSLI2lUkJ799/2wR9usDZkzSi9z/P4Jdzzxn5IoBoj3B7J7bbMnSifj1AKj59iwvC+mHfjcGT36xHT92ktzDEwOiQhtZZCsPLjo7hvwsUAEJOZyGpmXTUUt4zM1HRyq1+TmZaE836toT7vlzuKr3/hQ1SdaAYArC37Gqv3H8P7D1ytea07xYXnb83HnDUHwHMEokSDsxwm5PRHQVYaRj+zFV4bJ4TG8yIu6ZeCkl9fqxmStKu6XnNquXxgGnZUNwSv4zl5fgSAkMYyNckBgePgFaOszYUsbaK+b7j3jBdtJbxnJpK4+dBJbD50EjOuyMKTN18as/dqLcwxMDok7hQXFtyUa1pzHwqB4/DajsP4v4++sj3WU8HObtWsk1uNvi+IUoodVSeDTkHhsxPN+KDyOCbkaKWwiwsykHNRT5QeO42CgHaTgpkBC8X/fvgFFt+aj9z0nsEEsZlg4C8niCivO4PeKS5cMdQNd4oLZcdOhzSWmWlJwRGrdnHygMDzOOfVOhN1Ars9lFHbqpS15lQLiEXf2PLdRzFj1KAOc3JgjoHRYZk+aiBAgCferQAXmEAWbq/c4hPxx//I4zYVA/PAP0vBcwQuwbrPIdpwgvqEUXOqBUkOQdNEleQQsN5ifsOmyhMGxxDKOWWmJUW0Q9/62TcY+dstEDh5Her7mSWDp+SnB6+V38vcWCqf+WdjBge/63DwHLBkWgF6Jjkwc3kJPKoQodoIt0evQVuVssrfqfV/waXHTjPHwGDYIcUlgCNyhzONMmohUkAUKbyidZ9DNOEEvRGfPyXHtGpnREZPbDlorLK6Pqef5udwzmlndX1U4TW/hKCzmrOmDOmp3TBndRm8IrV0gjur6yGqPouDv9AzoXxmryiCJ/amkIoS8NDqA1g8dQQWT823NMLt1WsQb0kOQHZAs6/Jxotbq02fL+hAHdjMMTA6LGZVPrHAzOBHGk4wM+KL1lcGR40qVTsSBV7d8ZVByuKSfsmG00Io5wTI8ffWViB5/BS3LtsDfURD/Z0on039tYsixeaKr/HvypPwqRyKVWdGeqoTdY3awUuKIq7VICGgbQy0FfFU6FV44PpLsPfLBuz58pTm8RlXZHWY0wLAHAOjAxNpTN0u531+g8GPNJxgZcTzMlKxa954VNSdCYRMpKBEhpMnuDGvP4rz0w1OoaHJg8YWL87p5DdafH4kO3m5XyFGumZmt1E7wZpTLeB1bdgSgHX/PWG4ToLcU6E+yTh4gkduzEGv7g7cs+ITTU5BcUDhus07ahlnOOyUor758ytR8mUD3imtQ2qSgB9+L7NDOQWAOQZGB6a1EhNWSJRgV3U9igsy0NDkQUVdI860+MARuVP5SEMzctNTg5UydtemNq4nz5yHwBGoe3xdAo+7xgwxiLYpISmBI4awjESBSS9tBwGBJw79CgrzJ18YMVpe26iZChcOfXjLJ1LMfesA1s8eA6+uSqvF50d5bSNuW7a73ZvdYk0kpbaFg90oHOxu4xXah6mrMjo075bWYu5bB8ATEtZYzRo7CMt2fGXrvgIHDO/bA+XHQzfRfW9AKh6f9F0M7pNi2Am+W1qLh1eXwifKYaLs3t0xfnhf/HX3UXAcDPpPihIrAM2u+crff6BJxrY13Z08/jFzFDLTklBRdyYwj7t16+nhEvDK9Mvws7/tM/RlcASaz6tXuI2kAayjNItFo9zbFjB1VUanRB1zLq9rtJTpTnbymDwiA+f9EpbvPhr2vn4JYZ0CAHx6rBFT/7wn+LNAgCWB2QU7q+uh9lXV9edQrUynU0tfBKIyP71yEOasLsXWz+rh4gBwHEZmpbWrUwAAr19EeW0jpv35I/j91KJlLjLkfg4KJ8/Bp6qkcvBcINliDC+5U1wR7bo7ktJrLHohOoqTA5hjYCQASsw5f0AvTMztj/GLP0CjR2tMRSpLUzx586WYMWoQblv2ERqao5O/DoWfAve/WYovTjaZSmRYXQNAU9rpkQBIEj463GB+URvzm7XltqqL7CJJFAe/Pms45YkShb4NWl0Ga7dkuKMpvba2F6IjOTmAqasyEgx3igtlCyfhpR8VWKqkZvfrgf3zb8BfZozEzfkXmcpMtxarksNExMFxMXUKgByqe27TZ4bHH7p+GH4zJRdOniDZyWv+7eyotypE8tq2IFrlXkDr5M56/Djvk6u32kuFFmAnBkaCYqescUJOf/lPIE/BEWLouu1M6KuDAHnnRzgCngBekZr2HcQjwS+PyDZ6m6c3HgJHAJfAwStKeKI4N1gE0Njis2yq09MRB+9EW2rbVpIckcAcAyNhsVvWqP6F3ftlA57e2LpZ0x0VvVPIS++BJ27KxTmfBEAO7Ty/uQoC5J4CF09AOIKfXjnIdgdzrNapTJBbsLYcoAj2foiSBAdP0E3VpQ7IoznV5bTJTh73Xp2Nl7dWQeB4+CUR916djVPNXoNhthu7j0WMP5pS21BOrr3yDqwqidHlWLnnSMQaTJ0RgSP48x2X4XB9M57eeCjiuQ6xwsEB6gIul0Dw6oxCpKcmYc0nNfjLzi/h5ImstEupXAYsUgicXESgzMpWmgi7OeQQ07O3jAAFbMXu2zvGr1Tfqd/f7tpDEW1VEnMMjC5JQ5MH//qkBvuPnMLw/j1QXteILQe1g3LGXdwb0woHYMOBOrxXITd38QA4nTQ2o3V0EzicV7VZ93AJmDluCF7YXNUqZ+USCACi6Zw3KyHtKKWm6tMBgJisiZWrMhgR4E5xYea4oZipeqz6xFm8X3EcAA1OVQOAKfnpml/aXdX1mLOmrN3LTDsLEtWGUTx+P5Zsrmr9fSXAIWgLD8xi9x0lxq8OQ4VTt403cXMMhJBuALYDcAXeZw2ldIHq+YcBLAbQh1JaH691MBh2ye7Xw1KaQP1Lq+QslmyqwsqPw/dMMKzp5uBw79XZePGDqqA2U6zqA3wSBbGRzO6Iiez2XlM8y1U9AMZTSvMBFACYSAgZBQCEkAEArgPAfqsYCYk7xYUHrx8GByv4bjU35vUHIbEvKXbyHO4bf3HYEtLWlJrGioYmD8qOnQ6WqLb3muJ2YqBy8qIp8KMj8Ec5e78AYC6AtfF6fwYj3rhTXHh+WgHuf9N85jEjPD+9chAOHT8bVc6GQO6kFiXJtA+DEOD2oizcXpQVtrKnLVVd9ZVGVonv9lSajWvymRDCA9gPIBvAK5TSeYSQYgATKKW/JIR8BaDQLJRECJkFYBYAZGVljTxy5Ejc1slgtIaGJg9e3vo5Vuw52mqNoa6GLJkRfgCTHo4Af7itIGg4Nxyoxas7vgrex8ETPH9rvq0qnrYsCTXM8AjItMcr8d2hq5IIIb0AvA3glwBeBXA9pbQxlGNQw6qSGImAYmCUkZmZaUn4d8VxLFxXCQdP4BcpRElCjMdLdBk4AJPy+mH0xX2QkdY9OHNbMbY8IfD6Rdw9dgjuHjvElmGNZZlqOAdjVv3k5AmcAocmz4XESg+XgBV3FxlUeKOhQ1clUUpPE0I+BHAzgMEAygIxxUwAnxBCvk8pNZ9/yGAkCGbNTdOLBmJibn9NRZPchS13It979VCMHJgGgOCTI9/i5W1fBPSEGHokAO9VnMCmwDQ8iVLMvWE4lmyp0hjb//voK9w9dkjY+8VSb8mOgzGtfuI5w7jP9k58A/GtSuoDwBdwCkkArgXwDKW0r+o1X8HGiYHBSGTMKprMdpbjhvXBnVcMQkVdIwCCnZ9/g2U7vmynVXdMlDGtCk+/dwguwVwzKZxxj1WZql0HY1ZpJFKKBTflYNH6yjYfZxqKeJ4YLgLwt0CegQOwilK6Po7vx2AkBKFkE9wpLowbJu+dxg3rA78o4fWPWH4tFPrRr3Z33LEqCbXrYMymBM6fnIO89FSsnz0mGH5sb6cAxLcq6QCA74V5zaB4vT+D0RnoCEYiUXAJHAj5f+3de3SU9ZnA8e8zk2QIRIMGhQUUEWRpgiYKVVxZxBVRK7CoWKtuqUfXVnY9a6VFbcVy6mXPuoi0ll2Pl7anWg9y2Sqo66UoakGocgkY0GIQkISKEl0gEJLMzG//eN8J884lc3vnEuf5nJMjeeedd34/Bt9n5nd5HpL+xJ1qOdd4UgkwoW+Ma3e08Oy6XdzzQgNlAuIV5k2vdWVewQ2681mpAnZpzQDmvZ75LuBi0O4P8tNvjUxp8tiNJaGpBpiHXv3IUcujwwB+w6wl9XmrJxFJA4NSBWx4/+OYcf6pSVWlUzDvtb9w9TmDU7q5ppIRNd7Ko2QDzBNv74hb4MkfhK17DzJ+xElJtz1bdN+mUgXuvn88k5V3jOfCMwq3eHyhCAYNqz763FHkJnJXcbqW1zdzwUNv8k9P/ZkLHnqTFfXNjsdDVQbjBYWW1nYeeq37lO8H2zoyaqNb9BuDUgWupbWdwx0BHrnWmrK76382R2WCfXDaKDwCS9Z/yqY9B/PRzIIQMDB3xVbmLG9wLXU1uLO09ak/fUKgh+xh0cCgVAGLtT7+qe+dS+O+Q6xu3E+/Ch/nD6vqujldd94Qnl23mzkvNOStvoIbQrUV0hGqM/2jJfV4PB7a/ZnvU8h0aeuz63YnVQzp+PLSlNqVLTqUpFSB6q4W8O6Ww3z414OUl3qiN9WNHcL6ORNZeN3ZjBlSGKtcUtHH52XulG/Qu8zrON6rxMPsSSNYeF1d1N6FWDqD0UtZPSL87t1dNO47lFKbMlna2tLazs9f3JrwvFKvUDOwMqV2ZYsGBqUKVLyC91f+92pufnoDi9c3cfPTG7h0wVtRz62q8DG5diDLZl7AhjkTufH8IWm14biy3N8iAkHDuOEnEYxI13PUH+QXb3zMgTY/86ZbmUd9XisrqzfJ5KxHOgI8+mYjExe8w8+Wf5B0mzLJdtr0VRul3ui/Rw9WMCgv9eAr8TD/mtqCWJEEWsFNqYIVL7dOZAoFgF/PGM3F1QMSXq/pqzYa9h5gzvPJDTV5JLqWdDrKvIKIddOPlSvKK9C7rMQxD7CivjluQaQHrxzVlWqk0x/gO0+uSysH1co7xnfV4EgmmV46CfdivY+RfcjWxraCzpWklEpdrPXxdYMrWbfzq6hzX9+2L2FgCC3LrD2lL5fVDOCHizfxp49bun1OrxIvRzozr5zTGTAs/cFY1n7yJfMjqrP18Xl57IZzqCwvc9wgp9YNom/vUr7/9Hqr3nOYn7+4jctqBlB7Sl827/k/fCVe/GlU+Fnd+AWHOwI0NB/g/pettBQdgSC3XTSc6887NWbthlRv4OHvo1eEzkCQuVNquOG8IV2PFxoNDEoVsMj18fWffsW6nRuizptU3T+l61ZV+Hjm5rE07jvEb9fsilmJ7ryhJ7C56UDabQ9ngOufeo+5U6rxlXgcY/+d/iADK8tjVs/b82VbVFAAawgmNPE7+IRyAmmOfPz7/35IWYm3K7tpaHJ5/h+3s3DVx8ybnlzq7kTyWVshHTqUpFQPc+mCt/jLvsNdv/9t/z68dseEjK7Z0trO2h0tbNz9JR0Bw7S6gYwZWsXsJfUs3dic+AJJ6lXq4d7JVtI4EzS0B0zXMNPPJtcwalBl140z3hBM6DrhNQueXbebe15oSKktyax8crM2Qj7oUJJSReK1OybwxrbPeH3bPiZV9084hJSM0GT15NqBjuON+1vjPCNNQUNlr1Ienl7L7c9tAuiaM7nnhQZrMllg3vRahlT1iVoiClDigXuvqHbcrEcNqqTC53XUNQjxClEV3rwCJR6hPUHluFSWpOay4E+2aWBQqge6uHqAKwEhkW8OOZFNn7oznARwNGC4bdGmmDdroOtGPWtJPa/ePj5qiShYyfLuf3kbx/Uq6RrmGXxCeVT1PK8HFt8ylr0HjjJ72RbH8FVk+u54OgKBpJakulnwpxDoclWlVFw/uHBYVq6b6J7sD8LeA0e7loj28R3b03C4I+jY0wGxl5Mu+HYdY4ZWMbVuEE/OGEPvUm+8l4sraGBNY/flYrrbb9JT6TcGpVRcVRU+Hv1OHf/2XH3OX/tgW0fXpO2qjz5n7oqtXbuaIXqYJ9YEb2h4Z2BlL1KvLG2tpkq0W9qtgj+FRAODUqpbU+sGsefLIzlP//2jpVsIGuv1Lxp5MnOWOyeXY+08Dl9OGjm88+0xg1myvsm6iXf6iTGnHVOim7xbBX8KiQ4lKaUSurQm+/MZkdr9x4ZkQkNFvhKhd6kXX4l0u/M41vDOkvVN/P6mc5k7pZpXbh/PoMpeSbXjULufA20dcYeGMtkV7VbmV7fpNwalVELD+x/H2KEnxNxcl03hn9atgSCx15l2nwMj1vAOwPVP/RlfiZe2zkDUZHV3Zvzm/a4bfqxJ5VT2KbS0trN170HW7tjPb9bspMzrLbgJaw0MSqmkDDmxd84DQ2hIpnHfIWYv3exIBxJr7D80p9CnzBs1vBPaD9ER8KfVltCkcrz5hmR2RS+vb2bW4nrH5Hu73x+3P/migUEplZT3P81dUOhT5iVgDP959VmsbtzP7GVbonJERY79R84pTK39G17YtJdSr4fOoEGMSbhvIZFMJpVbWtujgoJb13abzjEopZJyRr+KnLyOV+DWC4fx0m3j6Nu7jDuXbaEjRoa8dn+APnZq7thzCs1dQeHHl4ywhqAylMmk8ta9B7tdpltIE9YaGJRSSTmlqk9OXidg4JdvbOeKX63m1mc2RNVUCPF4hMkLV7OivjlminKwivZ0+IM8snI7N10wNKN2JZrwTix+VMj82u7SwKCUSsplNakl6ktWeWn0bchvF9npLrPr0c5jG91izSmEK/V4OH9YVVIFfmK54dxTeffuizOaHK4ZWElpjMIRMy88PeNru00Dg1IqKWOGVvH3w6tcvebMCafz+HdH4ytJPM7Tu8xLmVfoVRpdvOhwR+DYLumy6B3OncEgNQMruwr8lCVb2QcrBcesSSMy/jRfVeFj/jW1+Eo8lJdafXnwylHcdfk3CuabQohmV1VKpWT9zhbu+sNmdnzRltF1fCUe3r3byly6or65q+5ERyBA0Fi7jo+dKzw5YwwDK8uZvHC1I+NqeAbU8GJE97+0LWbuovCVS680fMbCVY1xh6u6W6Karlwm20s3u6oGBqVUWlpa2xn9wMqkz//p5SN5ZOX2uInmwm+Yaxr3OwoUhZ8bHkS6W/+f7A04dN7OLw6x6L09HPX7ueacUzjzlBN6fKZUDQxKqZxbUd+cMI9SicAj19YxtW5QSp+Wuzv365TiOps0MCil8qKltZ1bfvc+G/dEp+e+YtQA7ps2Sm/eeaKFepRSeVFV4eMP/zrOTvVwgOav2mj3Bxk3vF/Mcp2q8GlgUEq5oqrCx/gRJ+e7GcoFulxVKaWUgwYGpZRSDhoYlFJKOWhgUEop5aCBQSmllEOP2McgIl8Au3PwUv2A/Tl4nUJRbP0F7XMxKLb+Qvw+DzHGnJTqxXpEYMgVEVmfzmaQnqrY+gva52JQbP0F9/usQ0lKKaUcNDAopZRy0MDg9ES+G5BjxdZf0D4Xg2LrL7jcZ51jUEop5aDfGJRSSjloYFBKKeVQlIFBRK4Rka0iEhSRMWHHLxGRDSLygf3ff4jx3BUi0pDbFmcu1T6LSG8ReVlEPrKf9x/5a33q0nmPRWS0fbxRRB4VkeQLAxeAbvpcJSKrRKRVRBZGPOc6u89bRORVEemX+5anL80+l4nIEyKy3f73fXXuW56edPobdk7S966iDAxAA3AV8E7E8f3AFGPMmcD3gGfCHxSRq4DWnLTQfen0+WFjzEjgbOACEbk8Jy11Rzr9fQz4PnCG/XNZDtrppnh9PgrcC/w4/KCIlAC/BC4yxpwFbAFuy0E73ZRSn233AJ8bY0YA1cDbWW2hu9Lpb8r3rqKsx2CM+RAg8gOhMWZT2K9bgV4i4jPGtItIBTAL68axJFdtdUsafT4CrLLP6RCRjcDgHDU3Y6n2FzgRON4Ys9Z+3tPANOCVnDTYBd30+TCwWkSGRzxF7J8+ItICHA805qCprkmjzwA3ASPt84L0oF3S6fQ3nXtXsX5jSMbVwCZjTLv9+/3AfOBI/pqUdZF9BkBE+gJTgDfy0qrsCe/vIKAp7LEm+9jXljGmE5gJfADsxfr0/Ou8NirL7H/LAPeLyEYRWSoi/fPaqOxL+d71tf3GICIrgQExHrrHGLM8wXNrgIeASfbvdcBwY8wdInKay011jZt9DjteAiwCHjXGfOJWW93gcn9jzScU3FruTPoc41qlWIHhbOAT4FfAT4AHMm2nm9zsM9Y9bzCwxhgzS0RmAQ8D382wma5x+T1O6971tQ0MxpiJ6TxPRAYDzwMzjDE77MPnA6NFZBfW39nJIvKWMWaCG211i8t9DnkC+NgY84tM2+c2l/vbhHOobDDWp+iCkm6f46izr7kDQESWAHe7eH1XuNznFqxPzs/bvy8Fbnbx+hlzub9p3bt0KCmM/TXzZeAnxpg1oePGmMeMMQONMacB44DthRYU0hWvz/ZjDwCVwA/z0bZs6OY9/itwSETG2quRZgCpfhrtaZqBahEJZd+8BPgwj+3JOmPt6H0RmGAfuhjYlrcGZVna9y5jTNH9AFdifUJsB/YBr9nH5wCHgfqwn5Mjnnsa0JDvPmS7z1ifmA3WjSJ0/J/z3Y9svsfAGKxVHzuAhdiZAXrKT7w+24/tAr7EWpnSBFTbx2+13+MtWDfMqnz3Iwd9HoK1qmcL1rzZqfnuRzb7G/Z40vcuTYmhlFLKQYeSlFJKOWhgUEop5aCBQSmllIMGBqWUUg4aGJRSSjloYFBFQURcT34oIlNF5G77z9NEpDqNa7wVniVTqUKggUGpNBljVhhjQunIp2HlGlKqx9PAoIqKWOaJSINdh+Ba+/gE+9P7MjtH/7Ohegwi8i372Gq7TsNL9vEbRWShiPwdMBWYJyL1IjIs/JuAiPSzUxIgIuUi8pxd/2AxUB7WtkkisjYsuVtFbv92lLJ8bXMlKRXHVVg5gmqBfsD7IhLKbX82UIOVI2kNVg2K9cDjwHhjzE4RWRR5QWPMuyKyAnjJGLMMotMih5kJHDHGnCUiZwEb7fP7Ye3KnmiMOSwid2GlSr7PjU4rlQoNDKrYjAMWGWMCwD4ReRv4JnAQeM8Y0wQgIvVYKQRagU+MMTvt5y/CymufrvHAowDGmC0issU+PhZrKGqNHVTKgLUZvI5SadPAoIpNd+U6w+tQBLD+/0i3vKefY0O1vSIei5WHRoA/GmOuS/P1lHKNzjGoYvMOcK2IeO2souOB97o5/yPg9LBc9tfGOe8QcFzY77uA0fafp0e8/g0AIjIKOMs+vg5r6Gq4/VhvERmRRH+Ucp0GBlVsnsfKqrkZeBO40xjzWbyTjTFtwL8Ar4rIaqyMlgdinPocMFtENonIMKziLzNF5F2suYyQx4AKewjpTuygZIz5ArgRWGQ/tg67/KRSuabZVZVKQEQqjDGt9iql/8IqXLQg3+1SKlv0G4NSid1iT0ZvxSpc9Hie26NUVuk3BqWUUg76jUEppZSDBgallFIOGhiUUko5aGBQSinloIFBKaWUw/8DMY3SQKzlmb0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制散点图\n",
    "housing.plot(kind='scatter', x='longitude', y = 'latitude')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22bdcee0af0>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 将alpha 设置为 0.1 ， 可以看出高密度数据点的位置\n",
    "housing.plot(kind='scatter', x='longitude', y = 'latitude', alpha=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\jiang\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\tools.py:298: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "c:\\users\\jiang\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\tools.py:298: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "c:\\users\\jiang\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\tools.py:304: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n",
      "c:\\users\\jiang\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\tools.py:304: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22bdce2e880>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# s 点的大小  la人多点大 c颜色  fig尺寸大小  cmap颜色域  col颜色域尺度\n",
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.4,\n",
    "    s=housing[\"population\"]/100, label=\"population\", figsize=(10,7),\n",
    "    c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"), colorbar=True,\n",
    "    sharex=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.image as mpimg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载图片\n",
    "california_img = mpimg.imread('./california.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 散点图\n",
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.4,\n",
    "    s=housing[\"population\"]/100, label=\"population\", figsize=(10,7),\n",
    "    c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"), colorbar=False,\n",
    "    sharex=False)\n",
    "# 绘制地图 extent绘制到三地图上的 横纵轴的起始位置 alpha 透明度  cmap 颜色域\n",
    "plt.imshow(california_img, extent=[-124.55, -113.80, 32.45, 42.05], alpha=0.5,\n",
    "           cmap=plt.get_cmap(\"jet\"))\n",
    "# x y的标识\n",
    "plt.ylabel(\"Latitude\", fontsize=14)\n",
    "plt.xlabel(\"Longitude\", fontsize=14)\n",
    "# 优化地图\n",
    "prices = housing[\"median_house_value\"]\n",
    "44\n",
    "# 均分矩阵\n",
    "tick_values = np.linspace(prices.min(), prices.max(), 11)\n",
    "cbar = plt.colorbar()\n",
    "# 取出值，并定义格式\n",
    "cbar.ax.set_yticklabels([\"$%dk\"%(round(v/1000)) for v in tick_values], fontsize=14)\n",
    "cbar.set_label('Median House Value', fontsize=16)\n",
    "\n",
    "plt.legend(fontsize=16)\n",
    "# save_fig(\"california_housing_prices_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 从图片中告诉我们，房屋价格与地理位置靠海，和人口密度息息相关"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 寻找特征相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value    1.000000\n",
       "median_income         0.688075\n",
       "total_rooms           0.134153\n",
       "housing_median_age    0.105623\n",
       "households            0.065843\n",
       "total_bedrooms        0.049686\n",
       "population           -0.024650\n",
       "longitude            -0.045967\n",
       "latitude             -0.144160\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# corr() 计算出每对特征之间的相关系数， 称为皮尔逊相关系数\n",
    "corr_matrix =  housing.corr()\n",
    "corr_matrix\n",
    "# 房价字段 进行排序\n",
    "corr_matrix['median_house_value'].sort_values(ascending = False)#以降序方式列出相关性大小"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\jiang\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\tools.py:298: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "c:\\users\\jiang\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\tools.py:298: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "c:\\users\\jiang\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\tools.py:304: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n",
      "c:\\users\\jiang\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\tools.py:304: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000022BDCD3CF10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000022BDCC8A640>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000022BDD011B20>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000022BDD7D1BE0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000022BDCD61A60>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000022BDCD60100>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000022BDCD60370>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000022BDD85F340>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000022BDCEA2B20>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000022BDD06A760>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000022BDD4072E0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000022BDCAB6400>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000022BDCE1F400>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000022BDCB33A00>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000022BDCB1D3D0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000022BDD0B3400>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用padans scatter_matrix 函数，可以绘制特征之间的相关性\n",
    "from pandas.plotting import scatter_matrix\n",
    "\n",
    "# 找到重要特征 绘制图表\n",
    "attributes = ['median_house_value', 'median_income', 'total_rooms', 'housing_median_age']\n",
    "scatter_matrix(housing[attributes], figsize = (12, 8) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 根据上述图形，可以看出收入和房价存在线性关系，单独绘出\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 16.0, 0.0, 550000.0)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind = 'scatter', x='median_income', y = 'median_house_value', alpha = 0.1)\n",
    "plt.axis([0, 16, 0, 550000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 50万美元是一条清晰的线，被设置上限了， 45w，35w，28w，也有虚线， 可能是异常值，可以在后期把数据删除掉"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   1. 在给机器学习算法输入数据之前，我们识别出了一些异常数据，需要提前清理掉，也发现了不同属性之间的某些联系，跟目标属性相关的联系\n",
    "   2. 某些属性分布显示出了明显的“重尾”分布，对进行转换处理，计算其对数\n",
    "   3. 在给机器学习算法输入数据之前，最后一件事儿，尝试各种属性的组合\n",
    "   4. 每个项目历程都不一样，大致思路都相识"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \\\n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY   \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY   \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY   \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY   \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY   \n",
       "\n",
       "  income_cat  \n",
       "0          5  \n",
       "1          5  \n",
       "2          5  \n",
       "3          4  \n",
       "4          3  "
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 试验不同特征的组合\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value          1.000000\n",
       "median_income               0.688075\n",
       "rooms_per_household         0.151948\n",
       "total_rooms                 0.134153\n",
       "housing_median_age          0.105623\n",
       "households                  0.065843\n",
       "total_bedrooms              0.049686\n",
       "population_per_household   -0.023737\n",
       "population                 -0.024650\n",
       "longitude                  -0.045967\n",
       "latitude                   -0.144160\n",
       "bedrooms_per_room          -0.255880\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建新的特征\n",
    "housing['rooms_per_household'] = housing['total_rooms']/housing['households']\n",
    "housing['bedrooms_per_room'] = housing['total_bedrooms']/housing['total_rooms']\n",
    "housing['population_per_household'] = housing['population']/housing['households']\n",
    "# 关联矩阵 查看相关性\n",
    "corr_matrix = housing.corr()\n",
    "corr_matrix['median_house_value'].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>286600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>340600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>196900.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>46300.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>254500.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "17606       710.0       339.0         2.7042            286600.0   \n",
       "18632       306.0       113.0         6.4214            340600.0   \n",
       "14650       936.0       462.0         2.8621            196900.0   \n",
       "3230       1460.0       353.0         1.8839             46300.0   \n",
       "3555       4459.0      1463.0         3.0347            254500.0   \n",
       "\n",
       "      ocean_proximity income_cat  \n",
       "17606       <1H OCEAN          2  \n",
       "18632       <1H OCEAN          5  \n",
       "14650      NEAR OCEAN          2  \n",
       "3230           INLAND          2  \n",
       "3555        <1H OCEAN          3  "
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 机器学习算法的数据准备\n",
    "# 得到一个分层抽样代表全局数据集的  训练集和测试集\n",
    "strat_train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16512 entries, 17606 to 15775\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype   \n",
      "---  ------              --------------  -----   \n",
      " 0   longitude           16512 non-null  float64 \n",
      " 1   latitude            16512 non-null  float64 \n",
      " 2   housing_median_age  16512 non-null  float64 \n",
      " 3   total_rooms         16512 non-null  float64 \n",
      " 4   total_bedrooms      16354 non-null  float64 \n",
      " 5   population          16512 non-null  float64 \n",
      " 6   households          16512 non-null  float64 \n",
      " 7   median_income       16512 non-null  float64 \n",
      " 8   ocean_proximity     16512 non-null  object  \n",
      " 9   income_cat          16512 non-null  category\n",
      "dtypes: category(1), float64(8), object(1)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "# 将数据集的数据和标签分开 \n",
    "housing_labels = strat_train_set['median_house_value'].copy()\n",
    "# 删除标签\n",
    "housing = strat_train_set.drop('median_house_value', axis = 1)\n",
    "housing.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4629</th>\n",
       "      <td>-118.30</td>\n",
       "      <td>34.07</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3759.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3296.0</td>\n",
       "      <td>1462.0</td>\n",
       "      <td>2.2708</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6068</th>\n",
       "      <td>-117.86</td>\n",
       "      <td>34.01</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4632.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3038.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>5.1762</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17923</th>\n",
       "      <td>-121.97</td>\n",
       "      <td>37.35</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>999.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>4.6328</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13656</th>\n",
       "      <td>-117.30</td>\n",
       "      <td>34.05</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2155.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>1.6675</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19252</th>\n",
       "      <td>-122.79</td>\n",
       "      <td>38.48</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6837.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3468.0</td>\n",
       "      <td>1405.0</td>\n",
       "      <td>3.1662</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "4629     -118.30     34.07                18.0       3759.0             NaN   \n",
       "6068     -117.86     34.01                16.0       4632.0             NaN   \n",
       "17923    -121.97     37.35                30.0       1955.0             NaN   \n",
       "13656    -117.30     34.05                 6.0       2155.0             NaN   \n",
       "19252    -122.79     38.48                 7.0       6837.0             NaN   \n",
       "\n",
       "       population  households  median_income ocean_proximity income_cat  \n",
       "4629       3296.0      1462.0         2.2708       <1H OCEAN          2  \n",
       "6068       3038.0       727.0         5.1762       <1H OCEAN          4  \n",
       "17923       999.0       386.0         4.6328       <1H OCEAN          4  \n",
       "13656      1039.0       391.0         1.6675          INLAND          2  \n",
       "19252      3468.0      1405.0         3.1662       <1H OCEAN          3  "
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#找出缺失值内容\n",
    "rows = housing[housing.isnull().any(axis = 1)]\n",
    "rows.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## total_bedrooms有缺失，因此全部设为none"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer(add_indicator=False, copy=True, fill_value=None,\n",
       "              missing_values=nan, strategy='median', verbose=0)"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建一个imputer实例，指定要用属性中的中位数值替换该属性的缺失值 \n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "# 获取中位数\n",
    "imputer = SimpleImputer(strategy = 'median')\n",
    "# 使用fit() 方法将 imputer实例适配到训练集\n",
    "# 删除非浮点\n",
    "housing_num = housing.drop('ocean_proximity', axis =1)\n",
    "# 估算出每一个列的中位数的值\n",
    "imputer.fit(housing_num)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.51  ,   34.26  ,   29.    , 2119.5   ,  433.    , 1164.    ,\n",
       "        408.    ,    3.5409,    3.    ])"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#中位数\n",
    "imputer.statistics_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.51  ,   34.26  ,   29.    , 2119.5   ,  433.    , 1164.    ,\n",
       "        408.    ,    3.5409,    3.    ])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 测试中位数\n",
    "housing_num.median().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-121.89  ,   37.29  ,   38.    , ...,  339.    ,    2.7042,\n",
       "           2.    ],\n",
       "       [-121.93  ,   37.05  ,   14.    , ...,  113.    ,    6.4214,\n",
       "           5.    ],\n",
       "       [-117.2   ,   32.77  ,   31.    , ...,  462.    ,    2.8621,\n",
       "           2.    ],\n",
       "       ...,\n",
       "       [-116.4   ,   34.09  ,    9.    , ...,  765.    ,    3.2723,\n",
       "           3.    ],\n",
       "       [-118.01  ,   33.82  ,   31.    , ...,  356.    ,    4.0625,\n",
       "           3.    ],\n",
       "       [-122.45  ,   37.77  ,   52.    , ...,  639.    ,    3.575 ,\n",
       "           3.    ]])"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = imputer.transform(housing_num)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "\n",
       "       population  households  median_income  income_cat  \n",
       "17606       710.0       339.0         2.7042         2.0  \n",
       "18632       306.0       113.0         6.4214         5.0  \n",
       "14650       936.0       462.0         2.8621         2.0  \n",
       "3230       1460.0       353.0         1.8839         2.0  \n",
       "3555       4459.0      1463.0         3.0347         3.0  "
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#使用dataframe查看更清楚直观\n",
    "housing_tr = pd.DataFrame(X, columns = housing_num.columns, index = housing_num.index)\n",
    "housing_tr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16512 entries, 17606 to 15775\n",
      "Data columns (total 9 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   longitude           16512 non-null  float64\n",
      " 1   latitude            16512 non-null  float64\n",
      " 2   housing_median_age  16512 non-null  float64\n",
      " 3   total_rooms         16512 non-null  float64\n",
      " 4   total_bedrooms      16512 non-null  float64\n",
      " 5   population          16512 non-null  float64\n",
      " 6   households          16512 non-null  float64\n",
      " 7   median_income       16512 non-null  float64\n",
      " 8   income_cat          16512 non-null  float64\n",
      "dtypes: float64(9)\n",
      "memory usage: 1.3 MB\n"
     ]
    }
   ],
   "source": [
    "housing_tr.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6563</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12053</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13908</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11159</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15775</th>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16512 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ocean_proximity\n",
       "17606       <1H OCEAN\n",
       "18632       <1H OCEAN\n",
       "14650      NEAR OCEAN\n",
       "3230           INLAND\n",
       "3555        <1H OCEAN\n",
       "...               ...\n",
       "6563           INLAND\n",
       "12053          INLAND\n",
       "13908          INLAND\n",
       "11159       <1H OCEAN\n",
       "15775        NEAR BAY\n",
       "\n",
       "[16512 rows x 1 columns]"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[['ocean_proximity']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     7276\n",
       "INLAND        5263\n",
       "NEAR OCEAN    2124\n",
       "NEAR BAY      1847\n",
       "ISLAND           2\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['ocean_proximity'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 4, ..., 1, 0, 3])"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将文本转化成对应的数字分类 ， 使用转换器\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder = LabelEncoder()\n",
    "housing_cat = housing['ocean_proximity']\n",
    "housing_cat_encoder = encoder.fit_transform(housing_cat)\n",
    "housing_cat_encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder.classes_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 机器学习算法会以为两个相近的数字比远的数字更相似，比如0，4 比  0，1相似度更高，\n",
    "2. 创建独热编码，当 <1H ,第0个属性为1，其余都为0， 列别是InLand时候，另一个属性为1，其余为0，1为热，0为冷，独热编码\n",
    "3. OneHotEncoder编码器, fit_trans需要二维数组， 转换housing_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 4, ..., 1, 0, 3])"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数字分类转化为 独热\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "# 实例\n",
    "encoder = OneHotEncoder()\n",
    "housing_cat_encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0],\n",
       "       [0],\n",
       "       [4],\n",
       "       ...,\n",
       "       [1],\n",
       "       [0],\n",
       "       [3]])"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_encoder.reshape(-1, 1)#行转换为列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<16512x5 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 16512 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_hot = encoder.fit_transform(housing_cat_encoder.reshape(-1, 1))\n",
    "housing_cat_hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 1.],\n",
       "       ...,\n",
       "       [0., 1., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 1., 0.]])"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从稀疏矩阵 转换成 numpy\n",
    "housing_cat_hot.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, 0, 0],\n",
       "       [1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 1],\n",
       "       ...,\n",
       "       [0, 1, 0, 0, 0],\n",
       "       [1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 1, 0]])"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelBinarizer\n",
    "encoder = LabelBinarizer()\n",
    "housing_cat_1hot = encoder.fit_transform(housing_cat)\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 自定义转换器\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "# 通过列名获取对应的索引\n",
    "rooms_ix, bedrooms_ix, population_ix, household_ix = [list(housing.columns).index(col) for col in (\"total_rooms\", \"total_bedrooms\", \"population\", \"households\")]\n",
    "rooms_ix, bedrooms_ix, population_ix, household_ix \n",
    "# 定义转化器的类\n",
    "class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n",
    "    # 初始化\n",
    "    def __init__(self, add_bedrooms_per_room = True):\n",
    "        self.add_bedrooms_per_room = add_bedrooms_per_room\n",
    "    # 训练数据\n",
    "    def fit(self, X, y = None):\n",
    "        return self\n",
    "    # 转化器\n",
    "    def transform(self, X, y = None):\n",
    "        # 房间数和\n",
    "        rooms_per_household = X[:, rooms_ix] / X[:, household_ix]\n",
    "        population_per_household = X[:, population_ix] / X[:, household_ix]\n",
    "        # 判端 是否添加卧室和房屋属的占比\n",
    "        if self.add_bedrooms_per_room:\n",
    "            bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]\n",
    "            return np.c_[X, rooms_per_household,population_per_household, bedrooms_per_room]\n",
    "        else:\n",
    "            # 将值添加到 np中，并返回\n",
    "            return np.c_[X, rooms_per_household,population_per_household]\n",
    "        \n",
    "        \n",
    "        \n",
    "attr_adder = CombinedAttributesAdder(add_bedrooms_per_room = False)\n",
    "# df-np\n",
    "housing_extra_attribs = attr_adder.transform(housing.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-121.89, 37.29, 38.0, ..., 2, 4.625368731563422,\n",
       "        2.094395280235988],\n",
       "       [-121.93, 37.05, 14.0, ..., 5, 6.008849557522124,\n",
       "        2.7079646017699117],\n",
       "       [-117.2, 32.77, 31.0, ..., 2, 4.225108225108225,\n",
       "        2.0259740259740258],\n",
       "       ...,\n",
       "       [-116.4, 34.09, 9.0, ..., 3, 6.34640522875817, 2.742483660130719],\n",
       "       [-118.01, 33.82, 31.0, ..., 3, 5.50561797752809,\n",
       "        3.808988764044944],\n",
       "       [-122.45, 37.77, 52.0, ..., 3, 4.843505477308295,\n",
       "        1.9859154929577465]], dtype=object)"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_extra_attribs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38</td>\n",
       "      <td>1568</td>\n",
       "      <td>351</td>\n",
       "      <td>710</td>\n",
       "      <td>339</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>4.62537</td>\n",
       "      <td>2.0944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14</td>\n",
       "      <td>679</td>\n",
       "      <td>108</td>\n",
       "      <td>306</td>\n",
       "      <td>113</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "      <td>6.00885</td>\n",
       "      <td>2.70796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-117.2</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31</td>\n",
       "      <td>1952</td>\n",
       "      <td>471</td>\n",
       "      <td>936</td>\n",
       "      <td>462</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>4.22511</td>\n",
       "      <td>2.02597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25</td>\n",
       "      <td>1847</td>\n",
       "      <td>371</td>\n",
       "      <td>1460</td>\n",
       "      <td>353</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>5.23229</td>\n",
       "      <td>4.13598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17</td>\n",
       "      <td>6592</td>\n",
       "      <td>1525</td>\n",
       "      <td>4459</td>\n",
       "      <td>1463</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>4.50581</td>\n",
       "      <td>3.04785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0      1   2     3     4     5     6       7           8  9       10  \\\n",
       "0 -121.89  37.29  38  1568   351   710   339  2.7042   <1H OCEAN  2  4.62537   \n",
       "1 -121.93  37.05  14   679   108   306   113  6.4214   <1H OCEAN  5  6.00885   \n",
       "2  -117.2  32.77  31  1952   471   936   462  2.8621  NEAR OCEAN  2  4.22511   \n",
       "3 -119.61  36.31  25  1847   371  1460   353  1.8839      INLAND  2  5.23229   \n",
       "4 -118.59  34.23  17  6592  1525  4459  1463  3.0347   <1H OCEAN  3  4.50581   \n",
       "\n",
       "        11  \n",
       "0   2.0944  \n",
       "1  2.70796  \n",
       "2  2.02597  \n",
       "3  4.13598  \n",
       "4  3.04785  "
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 转为df 添加了2个新的特征\n",
    "housing_tr = pd.DataFrame(housing_extra_attribs)\n",
    "housing_tr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38</td>\n",
       "      <td>1568</td>\n",
       "      <td>351</td>\n",
       "      <td>710</td>\n",
       "      <td>339</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
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       "      <td>2.0944</td>\n",
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       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14</td>\n",
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       "      <td>108</td>\n",
       "      <td>306</td>\n",
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       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>-117.2</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31</td>\n",
       "      <td>1952</td>\n",
       "      <td>471</td>\n",
       "      <td>936</td>\n",
       "      <td>462</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>4.22511</td>\n",
       "      <td>2.02597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25</td>\n",
       "      <td>1847</td>\n",
       "      <td>371</td>\n",
       "      <td>1460</td>\n",
       "      <td>353</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>5.23229</td>\n",
       "      <td>4.13598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17</td>\n",
       "      <td>6592</td>\n",
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       "      <td>4459</td>\n",
       "      <td>1463</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>4.50581</td>\n",
       "      <td>3.04785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      longitude latitude housing_median_age total_rooms total_bedrooms  \\\n",
       "17606   -121.89    37.29                 38        1568            351   \n",
       "18632   -121.93    37.05                 14         679            108   \n",
       "14650    -117.2    32.77                 31        1952            471   \n",
       "3230    -119.61    36.31                 25        1847            371   \n",
       "3555    -118.59    34.23                 17        6592           1525   \n",
       "\n",
       "      population households median_income ocean_proximity income_cat  \\\n",
       "17606        710        339        2.7042       <1H OCEAN          2   \n",
       "18632        306        113        6.4214       <1H OCEAN          5   \n",
       "14650        936        462        2.8621      NEAR OCEAN          2   \n",
       "3230        1460        353        1.8839          INLAND          2   \n",
       "3555        4459       1463        3.0347       <1H OCEAN          3   \n",
       "\n",
       "      rooms_per_household population_per_household  \n",
       "17606             4.62537                   2.0944  \n",
       "18632             6.00885                  2.70796  \n",
       "14650             4.22511                  2.02597  \n",
       "3230              5.23229                  4.13598  \n",
       "3555              4.50581                  3.04785  "
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_extra_attribs = pd.DataFrame(\n",
    "    housing_extra_attribs,\n",
    "    columns=list(housing.columns)+[\"rooms_per_household\", \"population_per_household\"],\n",
    "    index=housing.index)\n",
    "housing_extra_attribs.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "        ('imputer', SimpleImputer(strategy=\"median\")),\n",
    "        ('attribs_adder', CombinedAttributesAdder()),\n",
    "        ('std_scaler', StandardScaler())\n",
    "    ])\n",
    "\n",
    "housing_num_tr = num_pipeline.fit_transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16512 entries, 17606 to 15775\n",
      "Data columns (total 9 columns):\n",
      " #   Column              Non-Null Count  Dtype   \n",
      "---  ------              --------------  -----   \n",
      " 0   longitude           16512 non-null  float64 \n",
      " 1   latitude            16512 non-null  float64 \n",
      " 2   housing_median_age  16512 non-null  float64 \n",
      " 3   total_rooms         16512 non-null  float64 \n",
      " 4   total_bedrooms      16354 non-null  float64 \n",
      " 5   population          16512 non-null  float64 \n",
      " 6   households          16512 non-null  float64 \n",
      " 7   median_income       16512 non-null  float64 \n",
      " 8   income_cat          16512 non-null  category\n",
      "dtypes: category(1), float64(8)\n",
      "memory usage: 1.1 MB\n"
     ]
    }
   ],
   "source": [
    "housing_num.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataFrame -> series -> ndarray\n",
    "class DataFrameSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, attribute_names):\n",
    "        self.attribute_names = attribute_names\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X):\n",
    "        return X[self.attribute_names].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['longitude',\n",
       " 'latitude',\n",
       " 'housing_median_age',\n",
       " 'total_rooms',\n",
       " 'total_bedrooms',\n",
       " 'population',\n",
       " 'households',\n",
       " 'median_income',\n",
       " 'income_cat']"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_attribs =list(housing_num) \n",
    "num_attribs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_pipeline = Pipeline([\n",
    "        ('selector', DataFrameSelector(num_attribs)),\n",
    "        ('imputer', SimpleImputer(strategy=\"median\")),\n",
    "        ('attribs_adder', CombinedAttributesAdder()),\n",
    "        ('std_scaler', StandardScaler()),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import TransformerMixin \n",
    "class MyLabelBinarizer(TransformerMixin):\n",
    "    def __init__(self, *args, **kwargs):\n",
    "        self.encoder = LabelBinarizer(*args, **kwargs)\n",
    "    def fit(self, x, y=0):\n",
    "        self.encoder.fit(x)\n",
    "        return self\n",
    "    def transform(self, x, y=0):\n",
    "        return self.encoder.transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_attribs = ['ocean_proximity']\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "\n",
    "cat_pipeline = Pipeline([\n",
    "        ('selector', DataFrameSelector(cat_attribs)),               \n",
    "        ('LabelBinarizer', MyLabelBinarizer()),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import FeatureUnion\n",
    "\n",
    "full_pipeline = FeatureUnion(transformer_list=[\n",
    "        (\"num_pipline\", num_pipeline,),\n",
    "        ('cat_pipline', cat_pipeline),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.15604281,  0.77194962,  0.74333089, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-1.17602483,  0.6596948 , -1.1653172 , ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 1.18684903, -1.34218285,  0.18664186, ...,  0.        ,\n",
       "         0.        ,  1.        ],\n",
       "       ...,\n",
       "       [ 1.58648943, -0.72478134, -1.56295222, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 0.78221312, -0.85106801,  0.18664186, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-1.43579109,  0.99645926,  1.85670895, ...,  0.        ,\n",
       "         1.        ,  0.        ]])"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared = full_pipeline.fit_transform(housing)\n",
    "housing_prepared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.156043</td>\n",
       "      <td>0.771950</td>\n",
       "      <td>0.743331</td>\n",
       "      <td>-0.493234</td>\n",
       "      <td>-0.445438</td>\n",
       "      <td>-0.636211</td>\n",
       "      <td>-0.420698</td>\n",
       "      <td>-0.614937</td>\n",
       "      <td>-0.954456</td>\n",
       "      <td>-0.312055</td>\n",
       "      <td>-0.086499</td>\n",
       "      <td>0.155318</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.176025</td>\n",
       "      <td>0.659695</td>\n",
       "      <td>-1.165317</td>\n",
       "      <td>-0.908967</td>\n",
       "      <td>-1.036928</td>\n",
       "      <td>-0.998331</td>\n",
       "      <td>-1.022227</td>\n",
       "      <td>1.336459</td>\n",
       "      <td>1.890305</td>\n",
       "      <td>0.217683</td>\n",
       "      <td>-0.033534</td>\n",
       "      <td>-0.836289</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.186849</td>\n",
       "      <td>-1.342183</td>\n",
       "      <td>0.186642</td>\n",
       "      <td>-0.313660</td>\n",
       "      <td>-0.153345</td>\n",
       "      <td>-0.433639</td>\n",
       "      <td>-0.093318</td>\n",
       "      <td>-0.532046</td>\n",
       "      <td>-0.954456</td>\n",
       "      <td>-0.465315</td>\n",
       "      <td>-0.092405</td>\n",
       "      <td>0.422200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.017068</td>\n",
       "      <td>0.313576</td>\n",
       "      <td>-0.290520</td>\n",
       "      <td>-0.362762</td>\n",
       "      <td>-0.396756</td>\n",
       "      <td>0.036041</td>\n",
       "      <td>-0.383436</td>\n",
       "      <td>-1.045566</td>\n",
       "      <td>-0.954456</td>\n",
       "      <td>-0.079661</td>\n",
       "      <td>0.089736</td>\n",
       "      <td>-0.196453</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.492474</td>\n",
       "      <td>-0.659299</td>\n",
       "      <td>-0.926736</td>\n",
       "      <td>1.856193</td>\n",
       "      <td>2.412211</td>\n",
       "      <td>2.724154</td>\n",
       "      <td>2.570975</td>\n",
       "      <td>-0.441437</td>\n",
       "      <td>-0.006202</td>\n",
       "      <td>-0.357834</td>\n",
       "      <td>-0.004194</td>\n",
       "      <td>0.269928</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6  \\\n",
       "0 -1.156043  0.771950  0.743331 -0.493234 -0.445438 -0.636211 -0.420698   \n",
       "1 -1.176025  0.659695 -1.165317 -0.908967 -1.036928 -0.998331 -1.022227   \n",
       "2  1.186849 -1.342183  0.186642 -0.313660 -0.153345 -0.433639 -0.093318   \n",
       "3 -0.017068  0.313576 -0.290520 -0.362762 -0.396756  0.036041 -0.383436   \n",
       "4  0.492474 -0.659299 -0.926736  1.856193  2.412211  2.724154  2.570975   \n",
       "\n",
       "          7         8         9        10        11   12   13   14   15   16  \n",
       "0 -0.614937 -0.954456 -0.312055 -0.086499  0.155318  1.0  0.0  0.0  0.0  0.0  \n",
       "1  1.336459  1.890305  0.217683 -0.033534 -0.836289  1.0  0.0  0.0  0.0  0.0  \n",
       "2 -0.532046 -0.954456 -0.465315 -0.092405  0.422200  0.0  0.0  0.0  0.0  1.0  \n",
       "3 -1.045566 -0.954456 -0.079661  0.089736 -0.196453  0.0  1.0  0.0  0.0  0.0  \n",
       "4 -0.441437 -0.006202 -0.357834 -0.004194  0.269928  1.0  0.0  0.0  0.0  0.0  "
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared = pd.DataFrame(housing_prepared)\n",
    "housing_prepared.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17606    286600.0\n",
       "18632    340600.0\n",
       "14650    196900.0\n",
       "3230      46300.0\n",
       "3555     254500.0\n",
       "           ...   \n",
       "6563     240200.0\n",
       "12053    113000.0\n",
       "13908     97800.0\n",
       "11159    225900.0\n",
       "15775    500001.0\n",
       "Name: median_house_value, Length: 16512, dtype: float64"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(housing_prepared)\n",
    "df.head()\n",
    "housing_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(housing_prepared, housing_labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions: [203682.37379543 326371.39370781 204218.64588245  58685.4770482\n",
      " 194213.06443039]\n"
     ]
    }
   ],
   "source": [
    "# 预测数据\n",
    "some_data = housing.iloc[:5]\n",
    "some_labels = housing_labels.iloc[:5]\n",
    "\n",
    "some_data_prepared = full_pipeline.transform(some_data)\n",
    "\n",
    "print(\"Predictions:\", lin_reg.predict(some_data_prepared))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68376.64295459937"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "housing_predictions = lin_reg.predict(housing_prepared)\n",
    "lin_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "lin_mse\n",
    "lin_rmse = np.sqrt(lin_mse)\n",
    "lin_rmse\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\n",
       "                      max_features=None, max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                      random_state=42, splitter='best')"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg = DecisionTreeRegressor(random_state=42)\n",
    "tree_reg.fit(housing_prepared, housing_labels)\n",
    "#%%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_predictions = tree_reg.predict(housing_prepared)\n",
    "tree_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "tree_rmse = np.sqrt(tree_mse)\n",
    "tree_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([70274.7991723 , 67258.3624668 , 71350.42593227, 68882.91340979,\n",
       "       70987.99296566, 74177.52037059, 70788.57311306, 70850.53018019,\n",
       "       76430.62239321, 70212.6471067 ])"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "# neg_mean_squared_error‘ 也就是 均方差回归损失 该统计参数是预测数据和原始数据对应点误差的平方和的均值\n",
    "# 决策树交叉验证\n",
    "scores = cross_val_score(tree_reg, housing_prepared, housing_labels,\n",
    "                         scoring=\"neg_mean_squared_error\", cv=10)\n",
    "tree_rmse_scores = np.sqrt(-scores)\n",
    "tree_rmse_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [70274.7991723  67258.3624668  71350.42593227 68882.91340979\n",
      " 70987.99296566 74177.52037059 70788.57311306 70850.53018019\n",
      " 76430.62239321 70212.6471067 ]\n",
      "Mean: 71121.4387110585\n",
      "Standard deviation: 2434.3080046605132\n"
     ]
    }
   ],
   "source": [
    "def display_scores(scores):\n",
    "    print(\"Scores:\", scores)\n",
    "    print(\"Mean:\", scores.mean())\n",
    "    print(\"Standard deviation:\", scores.std())\n",
    "\n",
    "display_scores(tree_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [66877.52325028 66608.120256   70575.91118868 74179.94799352\n",
      " 67683.32205678 71103.16843468 64782.65896552 67711.29940352\n",
      " 71080.40484136 67687.6384546 ]\n",
      "Mean: 68828.99948449328\n",
      "Standard deviation: 2662.761570610345\n"
     ]
    }
   ],
   "source": [
    "lin_scores = cross_val_score(lin_reg, housing_prepared, housing_labels,\n",
    "                             scoring=\"neg_mean_squared_error\", cv=10)\n",
    "lin_rmse_scores = np.sqrt(-lin_scores)\n",
    "display_scores(lin_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=10, n_jobs=None, oob_score=False,\n",
       "                      random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "forest_reg = RandomForestRegressor(n_estimators=10, random_state=42)\n",
    "forest_reg.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22107.0888400092"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_predictions = forest_reg.predict(housing_prepared)\n",
    "forest_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "forest_rmse = np.sqrt(forest_mse)\n",
    "forest_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [51481.61843757 48867.18698326 53592.93924497 54917.35753217\n",
      " 50463.39403515 56776.52132037 51940.08691385 50521.84102811\n",
      " 55729.5024898  53136.5656865 ]\n",
      "Mean: 52742.701367175265\n",
      "Standard deviation: 2412.0829538615826\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "forest_scores = cross_val_score(forest_reg, housing_prepared, housing_labels,\n",
    "                                scoring=\"neg_mean_squared_error\", cv=10)\n",
    "forest_rmse_scores = np.sqrt(-forest_scores)\n",
    "display_scores(forest_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=RandomForestRegressor(bootstrap=True, ccp_alpha=0.0,\n",
       "                                             criterion='mse', max_depth=None,\n",
       "                                             max_features='auto',\n",
       "                                             max_leaf_nodes=None,\n",
       "                                             max_samples=None,\n",
       "                                             min_impurity_decrease=0.0,\n",
       "                                             min_impurity_split=None,\n",
       "                                             min_samples_leaf=1,\n",
       "                                             min_samples_split=2,\n",
       "                                             min_weight_fraction_leaf=0.0,\n",
       "                                             n_estimators=100, n_jobs=None,\n",
       "                                             oob_score=False, random_state=42,\n",
       "                                             verbose=0, warm_start=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid=[{'max_features': [2, 4, 6, 8],\n",
       "                          'n_estimators': [3, 10, 30]},\n",
       "                         {'bootstrap': [False], 'max_features': [2, 3, 4],\n",
       "                          'n_estimators': [3, 10]}],\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "             scoring='neg_mean_squared_error', verbose=0)"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [\n",
    "    {'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]},\n",
    "    {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]},\n",
    "  ]\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state=42)\n",
    "grid_search = GridSearchCV(forest_reg, param_grid, cv=5,\n",
    "                           scoring='neg_mean_squared_error', return_train_score=True)\n",
    "grid_search.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 6, 'n_estimators': 30}"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_  #利用网格搜索选出最佳参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features=6, max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=30, n_jobs=None, oob_score=False,\n",
       "                      random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_ #利用网格搜索选出最好的估算器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64246.880700613496 {'max_features': 2, 'n_estimators': 3}\n",
      "55869.85367924813 {'max_features': 2, 'n_estimators': 10}\n",
      "53472.1282558969 {'max_features': 2, 'n_estimators': 30}\n",
      "61376.36445082522 {'max_features': 4, 'n_estimators': 3}\n",
      "53846.329115303764 {'max_features': 4, 'n_estimators': 10}\n",
      "51270.1941502407 {'max_features': 4, 'n_estimators': 30}\n",
      "59860.61532587693 {'max_features': 6, 'n_estimators': 3}\n",
      "53114.42460001889 {'max_features': 6, 'n_estimators': 10}\n",
      "50811.43543872171 {'max_features': 6, 'n_estimators': 30}\n",
      "59220.31563298743 {'max_features': 8, 'n_estimators': 3}\n",
      "52884.78697544277 {'max_features': 8, 'n_estimators': 10}\n",
      "50944.39369116168 {'max_features': 8, 'n_estimators': 30}\n",
      "62805.52917192821 {'bootstrap': False, 'max_features': 2, 'n_estimators': 3}\n",
      "54462.1410888642 {'bootstrap': False, 'max_features': 2, 'n_estimators': 10}\n",
      "61117.32056104296 {'bootstrap': False, 'max_features': 3, 'n_estimators': 3}\n",
      "53022.992252269294 {'bootstrap': False, 'max_features': 3, 'n_estimators': 10}\n",
      "60234.58052562756 {'bootstrap': False, 'max_features': 4, 'n_estimators': 3}\n",
      "52712.989031117104 {'bootstrap': False, 'max_features': 4, 'n_estimators': 10}\n"
     ]
    }
   ],
   "source": [
    "cvres = grid_search.cv_results_\n",
    "for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
    "    print(np.sqrt(-mean_score), params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint\n",
    "\n",
    "param_distribs = {\n",
    "        'n_estimators': randint(low=1, high=200),\n",
    "        'max_features': randint(low=1, high=8),\n",
    "    }\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state=42)\n",
    "rnd_search = RandomizedSearchCV(forest_reg, param_distributions=param_distribs,\n",
    "                                n_iter=10, cv=5, scoring='neg_mean_squared_error', random_state=42)\n",
    "rnd_search.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cvres = rnd_search.cv_results_\n",
    "for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
    "    print(np.sqrt(-mean_score), params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importances = grid_search.best_estimator_.feature_importances_\n",
    "feature_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# num_attribs\n",
    "extra_attribs = [\"rooms_per_household\", \"population_per_household\", \"bedrooms_per_room\"]\n",
    "cat_one_hot_attribs = list(encoder.classes_)\n",
    "attributes = num_attribs + extra_attribs + cat_one_hot_attribs\n",
    "sorted(zip(feature_importances, attributes), reverse = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model = grid_search.best_estimator_\n",
    "\n",
    "X_test = strat_test_set.drop('median_house_value', axis = 1)\n",
    "y_test = strat_test_set['median_house_value'].copy()\n",
    "\n",
    "X_test_prepared = full_pipeline.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_predictions = final_model.predict(X_test_prepared)\n",
    "\n",
    "final_mse = mean_squared_error(y_test, final_predictions)\n",
    "final_rmse = np.sqrt(final_mse)\n",
    "final_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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